A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

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Abstract:
In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.
, Available online  , doi: 10.1109/JAS.2019.1911546 doi: 10.1109/JAS.2019.1911546
Abstract:
In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly, a multi-expert system consisting of color component dispersion, similarity and centroid motion is established to identify flames. The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.
, Available online  , doi: 10.1109/JAS.2019.1911660 doi: 10.1109/JAS.2019.1911660
Abstract:
In minimally invasive surgery, one of the main objectives is to ensure safety and target reaching accuracy during needle steering inside the target organ. In this research work, the needle steering approach is determined using a robust control algorithm namely the Integral sliding mode control (ISMC) strategy to eliminate the chattering problem associated with the general clinical scenario. In general, the discontinuity component of feedback control input is not appropriate for the needle steering methodology due to the practical limitations of the driving actuators. Thus in ISMC, we have incorporated the replacement of the discontinuous component using a super twisting control (STC) input due to its unique features of chattering elimination and disturbance observation characteristics. In our study, the kinematic model of an asymmetric flexible bevel-tip needle in a soft-tissue phantom is used to evaluate stability analysis. A comparative study based on the analysis of chattering elimination is executed to determine the performance of the proposed control strategy in real-time needle steering with conventional sliding mode control using vision feedback through simulation and experimental results. This validates the efficacy of the proposed control strategy for clinical needle steering.
, Available online  , doi: 10.1109/JAS.2019.1911558 doi: 10.1109/JAS.2019.1911558
Abstract:
In this paper, a delay-dependent anti-windup compensator is designed for wide-area power systems to enhance the damping of inter-area low-frequency oscillations in the presence of time-varying delays and actuator saturation using an indirect approach. In this approach, first, a conventional wide-area damping controller is designed by using \begin{document}$H_{\infty}$\end{document} output feedback with regional pole placement approach without considering time-varying delays and actuator saturation. Then to mitigate the effect of both time-varying delays and actuator saturation, an add-on delay-dependent anti-windup compensator is designed. Based on generalized sector conditions, less conservative delay-dependent sufficient conditions are derived in the form of a linear matrix inequality (LMI) to guarantee the asymptotic stability of the closed-loop system in the presence of time-varying delays and actuator saturation by using Lyapunov-Krasovskii functional and Jensen integral inequality. Based on sufficient conditions, the LMI-based optimization problem is formulated and solved to obtain the compensator gain which maximizes the estimation of the region of attraction and minimizes the upper bound of \begin{document}$L_{2}$\end{document}-gain. Nonlinear simulations are performed first using MATLAB/Simulink on a two-area four-machine power system to evaluate the performance of the proposed controller for two operating conditions, e.g., 3-phase to ground fault and generator 1 terminal voltage variation. Then the proposed controller is implemented in real-time on an OPAL-RT digital simulator. From the results obtained it is verified that the proposed controller provides sufficient damping to the inter-area oscillations in the presence of time-varying delays and actuator saturation and maximizes the estimation of the region of attraction.
, Available online  , doi: 10.1109/JAS.2019.1911516 doi: 10.1109/JAS.2019.1911516
Abstract:
This paper presents the development of a new robust optimal decentralized PI controller based on nonlinear optimization for liquid level control in a coupled tank system. The proposed controller maximizes the closed-loop bandwidth for specified gain and phase margins, with constraints on the overshoot ratio to achieve both closed-loop performance and robustness. In the proposed work, a frequency response fitting model reduction technique is initially employed to obtain a first order plus dead time (FOPDT) model of each higher order subsystem. Furthermore, based on the reduced order model, a proposed controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed optimal robust decentralized control scheme has been compared with that of a decentralized PI controller. The proposed controller is implemented in real-time on a coupled tank system. From the obtained results, it is shown that the proposed optimal decentralized PI controller exhibits superior control performance to maintain the desired level, for both the nominal as well as the perturbed case as compared to a decentralized PI controller.
, Available online  , doi: 10.1109/JAS.2019.1911654 doi: 10.1109/JAS.2019.1911654
Abstract:
In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been modeled into a general nonlinear input affine system. Initially, the global asymptotic stability is ensured with conventional periodic sampling approach for reference trajectory tracking. Then the proposed approach of event-triggered sliding mode control is discussed which guarantees semi-global uniform ultimate boundedness. The proposed control approach guarantees non-accumulation of control updates ensuring lower bounds on inter-event triggering instants avoiding Zeno behavior in presence of the disturbance. The system shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost. The simulation results are provided for validation of proposed methodology for tracking problem by a robotic manipulator. The number of aperiodic control updates is found to be approximately 44% and 61% in the presence of constant and time-varying disturbances respectively.
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Abstract:
To overcome the shortcomings of 1D and 2D Otsu’s thresholding techniques, the 3D Otsu method has been developed. Among all Otsu’s methods, 3D-Otsu technique provides the best threshold values for the multi-level thresholding processes. In this paper, to improve the quality of segmented images, a simple and effective multilevel thresholding method is introduced. The proposed approach focuses on preserving edge detail by computing the 3D Otsu along the fusion phenomena. The advantages of the presented scheme include higher quality outcomes, better preservation of tiny details and boundaries and reduced execution time with rising threshold levels. The fusion approach depends upon the differences between pixel intensity values within a small local space of an image; it aims to improve localized information after the thresholding process. The fusion of images based on local contrast can improve image segmentation performance by minimizing the loss of local contrast, loss of details and gray-level distributions. Results show that the proposed method yields more promising segmentation results when compared to conventional 1D Otsu, 2D Otsu and 3D Otsu methods, as evident from the objective and subjective evaluations.
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Abstract:
This paper discusses a novel rational approximation algorithm of arbitrary-order fractances, which has high order-stability characteristic and wider approximation frequency bandwidth. The fractor has been exploited extensively in various scientific domains. The well-known shortcoming of the existing fractance approximation circuits, such as the oscillation phenomena, is still in great need of special research attention. Motivated by this need, a novel algorithm with high order-stability characteristic and wider approximation frequency bandwidth is introduced. In order to better understand the iterating process, the approximation principle of this algorithm is investigated at first. Next, features of the iterating function and frequency-domain characteristics of the impedance function calculated by this algorithm are researched, respectively. Furthermore, approximation performance comparisons have been made between the corresponding circuit and other types of fractance approximation circuits. Finally, a fractance approximation circuit with the impedance function of negative 2/3-order is designed. The high order-stability characteristic and wider approximation frequency bandwidth are fundamental important advantages, which make our proposed algorithm competitive in practical applications.
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Abstract:
Coronary artery systems are a kind of complex biological systems. Their chaotic phenomena can lead to serious health problems and illness development. From the perspective of engineering, this paper investigates the chaos suppression problem. At first, nonlinear dynamics of coronary artery systems are presented. To suppress the chaotic phenomena, the method of derivative-integral terminal sliding mode control is adopted. Since coronary artery systems suffer from uncertainties, the technique of disturbance observer is taken into consideration. The stability of such a control system that integrates the derivative-integral terminal sliding mode controller and the disturbance observer is proven in the sense of Lyapunov. To verify the feasibility and effectiveness of the proposed strategy, simulation results are illustrated in comparison with a benchmark.
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Abstract:
For the problem of set point regulation of the liquid level in coupled tank systems, we present a continuous sliding mode control (SMC) with a " conditional integrator”, which only provides integral action inside the boundary layer. For a special choice of the controller parameters, our design can be viewed as a PID controller with anti-windup and achieves robust regulation. The proposed controller recovers the transient response performance without control chattering. Both full-state feedback as well as output-feedback designs are presented in this work. Our output-feedback design uses a high-gain observer (HGO) which recovers the performance of a state-feedback design where plant parameters are assumed to be known. We consider both interacting as well as non-interacting tanks and analytical results for stability and transient performance are presented in both the cases. The proposed controller continuous SMC with conditional integrators (CSMCCI) provides superior results in terms of the performance measures as well as performance indices than ideal SMC, continuous SMC (CSMC) and continuous SMC with conventional integrator (CSMCI). Experimental results demonstrate good tracking performance in spite of unmodeled dynamics and disturbances.
, Available online
Abstract:
Providing autonomous systems with an effective quantity and quality of information from a desired task is challenging. In particular, autonomous vehicles, must have a reliable vision of their workspace to robustly accomplish driving functions. Speaking of machine vision, deep learning techniques, and specifically convolutional neural networks, have been proven to be the state of the art technology in the field. As these networks typically involve millions of parameters and elements, designing an optimal architecture for deep learning structures is a difficult task which is globally under investigation by researchers. This study experimentally evaluates the impact of three major architectural properties of convolutional networks, including the number of layers, filters, and filter size on their performance. In this study, several models with different properties are developed, equally trained, and then applied to an autonomous car in a realistic simulation environment. A new ensemble approach is also proposed to calculate and update weights for the models regarding their mean squared error values. Based on design properties, performance results are reported and compared for further investigations. Surprisingly, the number of filters itself does not largely affect the performance efficiency. As a result, proper allocation of filters with different kernel sizes through the layers introduces a considerable improvement in the performance. Achievements of this study will provide the researchers with a clear clue and direction in designing optimal network architectures for deep learning purposes.
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Abstract:
In this paper, some computational tools are proposed to determine the largest invariant set, with respect to either a continuous-time or a discrete-time system, that is contained in an algebraic set. In particular, it is shown that if the vector field governing the dynamics of the system is polynomial and the considered analytic set is a variety, then algorithms from algebraic geometry can be used to solve the considered problem. Examples of applications of the method (spanning from the characterization of the stability to the computation of the zero dynamics) are given all throughout the paper.
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Abstract:
The conventional Kalman filter is based on the assumption of non-delayed measurements. Several modifications appear to address this problem, but they are constrained by two crucial assumptions: i) the delay is an integer multiple of the sampling interval, and ii) a stochastic model representing the relationship between delayed measurements and a sequence of possible non-delayed measurements is known. Practical problems often fail to satisfy these assumptions, leading to poor estimation accuracy and frequent track-failure. This paper introduces a new variant of the Kalman filter, which is free from the stochastic model requirement and addresses the problem of fractional delay. The proposed algorithm fixes the maximum delay (problem specific), which can be tuned by the practitioners for varying delay possibilities. A sequence of hypothetically defined intermediate instants characterizes fractional delays while maximum likelihood based delay identification could preclude the stochastic model requirement. Fractional delay realization could help in improving estimation accuracy. Moreover, precluding the need of a stochastic model could enhance the practical applicability. A comparative analysis with ordinary Kalman filter shows the high estimation accuracy of the proposed method in the presence of delay.
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Abstract:
In this paper, we study the consensus problem for a class of linear multi-agent systems (MASs) with consideration of input saturation under the self-triggered mechanism. In the context of discrete-time systems, a self-triggered strategy is developed to determine the time interval between the adjacent triggers. The triggering condition is designed by using the current sampled consensus error. Furthermore, the consensus control protocol is designed by means of a state feedback approach. It is shown that the considered multi-agent systems can reach consensus with the presented algorithm. Some sufficient conditions are proposed in the form of linear matrix inequalities (LMIs) to show the positively invariant property of the domain of attraction (DOA). Moreover, some sufficient conditions of controller synthesis are provided to enlarge the volume of the DOA and obtain the control gain matrix. A numerical example is simulated to demonstrate the effectiveness of the theoretical analysis results.
, Available online
Abstract:
This paper investigates the distributed model predictive control (MPC) problem of linear systems whose network topologies are changeable by the way of inserting new subsystems, disconnecting existing subsystems, or merely modifying the couplings between different subsystems. To equip live systems with the quick response ability when modifying network topology, while keeping a satisfactory dynamic performance, a novel reconfiguration control scheme based on the alternating direction method of multipliers (ADMM) is presented. In this scheme, the local controllers directly influenced by the structure realignment are redesigned in the reconfiguration control. Meanwhile, by employing the powerful ADMM algorithm, the iterative formulas for solving the reconfigured optimization problem are obtained, which significantly accelerate the computation speed and ensure a timely output of the reconfigured optimal control response. Ultimately, the presented reconfiguration scheme is applied to the level control of a benchmark four-tank plant to illustrate its effectiveness and main characteristics.
, Available online  , doi: 10.1109/JAS.2019.1911801 doi: 10.1109/JAS.2019.1911801
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Random vector functional link networks (RVFL) is a class of single hidden layer neural networks based on a learner paradigm by which some parameters are randomly selected and contains more information due to the direct links between inputs and outputs. In this paper, combining the advantages of RVFL and the ideas of online sequential extreme learning machine (OS-ELM) and initial-training-free online extreme learning machine (ITF-OELM), a novel online learning algorithm which is named as initial-training-free online random vector functional link (ITF-ORVFL) is investigated for training RVFL. Because the idea of ITF-ORVFL comes from OS-ELM and ITF-OELM, the link vector of RVFL can be analytically determined based on sequentially arriving data by ITF-ORVFL with a high learning speed. Besides a novel variable is added to the update formulae of ITF-ORVFL, and the stability for nonlinear systems based on this learning algorithm is guaranteed. The experiment results indicate that the proposed ITF-ORVFL is effective in estimating nonparametric uncertainty.
, Available online  , doi: 10.1109/JAS.2019.1911810 doi: 10.1109/JAS.2019.1911810
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In this paper, interval type-2 fuzzy sets, fuzzy comprehensive evaluation and the fuzzy control rules are synthesized to realize the control of unmanned vehicle in driving state and behavioral decisions. Compared to the type-1 fuzzy set, type-2 fuzzy sets have more advantages in handling the model based on uncertainties, linguistic information because the membership functions are fuzzy sets. Different membership functions are established for each factor when the unmanned vehicle is driving at different speed intervals. In addition, a new evaluation method is developed to analyze unmanned vehicle’s driving state. Finally, a set of dynamic fuzzy rules are sorted out, which can be applied to the unmanned vehicle’s behavioral decision-making and provide a new idea to related research.
, Available online
Abstract:
This paper presents an output feedback design approach based on the adaptive control scheme developed for nonlinearly parameterized systems, to achieve global output regulation for a class of nonlinear systems in output feedback form. We solve the output regulation problem without the knowledge of the sign and the value of the high frequency gain a priori. It is not necessary to have both the limiting assumptions that the exogenous signal ω and the unknown parameter μ belong to a prior known compact set and the high frequency gain has a determinate lower and upper bounds. The effectiveness of the proposed algorithm is shown with the help of an example.
, Available online
Abstract:
MAximizing the lifetime of wireless sensor networks is an important and challenging research problem. Properly scheduling the movements of mobile sinks to balance the energy consumption of wireless sensor network is one of the most effective approaches to prolong the lifetime of wireless sensor networks. However, the existing mobile sink scheduling methods either require a great amount of computational time or lack of effectiveness in finding high-quality scheduling solutions. To address the above issues, this paper proposes a novel hyperheuristic framework, which can automatically construct highlevel heuristics to schedule the sink movements and prolong the network lifetime. In the proposed framework, a set of low-level heuristics are defined as building blocks to construct high-level heuristics and a set of random networks with different features are designed for training. Further, a genetic programming algorithm is adopted to automatically evolve promising highlevel heuristics based on the building blocks and the training networks. By using the genetic programming to evolve more effective heuristics and applying these heuristics in a greedy scheme, our proposed hyper-heuristic framework can prolong the network lifetime competitively with other methods, with small time consumption. A series of comprehensive experiments, including both static and dynamic networks, are designed. The simulation results have demonstrated that the proposed method can offer a very promising performance in terms of network lifetime and response time.
, Available online
Abstract:
Parking into small berths remains difficult for unskilled drivers. Researchers had proposed different automatic parking systems to solve this problem. The first kind of strategies (called parking trajectory planning) designs a detailed reference trajectory that links the start and ending points of a special parking task and let the vehicle track this reference trajectory so as to park into the berth. The second kind of strategies (called guidance control) just characterizes several regimes of driving actions as well as the important switching points in certain rule style and let the vehicle follows the pre-selected series of actions so as to park into the berth. Parking guidance control is simpler than parking trajectory planning. However, no studies thoroughly validated parking guidance control before. In this paper, a new automatic parking method is presented, which could characterize the desired control actions directly. Then the feasibility is examined carefully. Tests show that a simple parking guidance control strategy can work in most parallel parking tasks, if the available parking berth is not too small. This finding helps to build more concise automatic parking systems that can efficiently guide human drivers.
, Available online
Abstract:
For a single machine infinite power system with thyristor controlled series compensation (TCSC) device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network (RBFNN). The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness (UUB) of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.
, Available online
Abstract:
Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers. Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis, i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors' mathematical models with linear and nonlinear drift. Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.
, Available online  , doi: 10.1109/JAS.2020.1911804 doi: 10.1109/JAS.2020.1911804
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Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.
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Abstract:
By virtue of alternating direction method of multipliers (ADMM), Newton-Raphson method, ratio consensus approach and running sum method, two distributed iterative strategies are presented in this paper to address the economic dispatch problem (EDP) in power systems. Different from most of the existing distributed ED approaches which neglect the effects of packet drops or/and time delays, this paper takes into account both packet drops and time delays which frequently occur in communication networks. Moreover, directed and possibly unbalanced graphs are considered in our algorithms, over which many distributed approaches fail to converge. Furthermore, the proposed schemes can address the EDP with local constraints of generators and nonquadratic convex cost functions, not just quadratic ones required in some existing ED approaches. Both theoretical analyses and simulation studies are provided to demonstrate the effectiveness of the proposed schemes.
, Available online  , doi: 10.1109/JAS.2019.1911735 doi: 10.1109/JAS.2019.1911735
Abstract:
Many effective optimization algorithms require partial derivatives of objective functions, while some optimization problems' objective functions have no derivatives. According to former research studies, search direction is obtained using the quadratic hypothesis of objective functions. Based on derivatives, quadratic function assumptions, and directional derivatives, the computational commonalities of numerical first-order partial derivatives, second-order partial derivatives, and numerical second-order mixed partial derivatives were constructed. Based on the coordinate transformation relation, a set of orthogonal vectors in the fixed coordinate system was established according to the optimization direction. A numerical algorithm was proposed, taking the second order approximation direction as an example. A large stepsize numerical algorithm based on coordinate transformation was proposed. Several algorithms were validated by an unconstrained optimization of the two-dimensional Rosenbrock objective function. The numerical second order approximation direction with the numerical mixed partial derivatives showed good results. The calculated amount accounts for 0.2843% of the calculated value of the second-order mixed partial derivative. In the process of rotating the local coordinate system 360°, because the objective function is more complex than the quadratic function, if the numerical direction derivative is used instead of the analytic partial derivative, the optimization direction varies with a range of 103.05°. Because theoretical error is in the numerical negative gradient direction, the calculation with the coordinate transformation is 94.71% less than the calculation without coordinate transformation. If there is no theoretical error in the numerical negative gradient direction or in the large-stepsize numerical optimization algorithm based on the coordinate transformation, the sawtooth phenomenon occurs. When each numerical mixed partial derivative takes more than one point, the optimization results cannot be improved. The numerical direction based on the quadratic hypothesis only requires the objective function to be obtained, but does not require derivability and does not take into account truncation error and rounding error. Thus, the application scopes of many optimization methods are extended.
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Abstract:
Fault and delay accommodating simultaneously for a class of linear systems subject to state delays, actuator faults and disturbances is investigated in this work. A matrix norm minimization technique is applied to minimize the norms of coefficient matrix on time delay terms of the system in consideration. Compared with the matrix inequality scaling technique, the minimization technique can relax substantially the obtained stability conditions for state delay systems, especially, when the coefficient matrices of time delay terms have a large order of magnitudes. An output feedback adaptive fault-delay tolerant controller (AFDTC) is designed subsequently to stabilize the plant with state delays and actuator faults. Compared with the conventional fault tolerant controller (FTC), the designed output feedback AFDTC can be updated on-line to compensate the effect of both faults and delays on systems. Simulation results under two numerical examples exhibit the effectiveness and merits of the proposed method.
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Abstract:
Embedded systems have numerous applications in everyday life. Petri-net-based Representation for Embedded Systems (PRES+) is an important methodology for the modeling and analysis of these embedded systems. For a large complex embedded system, the state space explosion is a difficult problem for PRES+ to model and analyze. The Petri net synthesis method allows one to bypass the state space explosion issue. To solve this problem, as well as model and analyze large complex systems, two synthesis methods for PRES+ are presented in this paper. First, the property preservation of the synthesis shared transition set method is investigated. The property preservation of the synthesis shared transition subnet set method is then studied. An abstraction-synthesis-refinement representation method is proposed. Through this representation method, the synthesis shared transition set approach is used to investigate the property preservation of the synthesis shared transition subnet set operation. Under certain conditions, several important properties of these synthetic nets are preserved, namely reachability, timing, functionality, and liveness. An embedded control system model is used as an example to illustrate the effectiveness of these synthesis methods for PRES+.
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Abstract:
Integrated circuit chips are produced on silicon wafers. Robotic cluster tools are widely used since they provide a reconfigurable and efficient environment for most wafer fabrication processes. Recent advances in new semiconductor materials bring about new functionality for integrated circuits. After a wafer is processed in a processing chamber, the wafer should be removed there as fast as possible to guarantee its high-quality integrated circuits. Meanwhile, maximization of the throughput of robotic cluster tools is desired. This work aims to perform post-processing time-aware scheduling for such tools subject to wafer residency time constraints. To do so, closed-form expression algorithms are derived to compute robot waiting time accurately upon the analysis of particular events of robots waiting for single-arm cluster tools. Examples are given to show the application and effectiveness of our proposed algorithms.
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Abstract:
In this paper, a Large Dynamic Range Floating Memristor Emulator (LDRFME) with equal port current restriction is proposed to be achieved by a large dynamic range floating Voltage-Controlled Linear Resistor (VCLR). Since real memristors have not been largely commercialized until now, the application of a LDRFME to memristive systems is reasonable. Motivated by this need, this paper proposes an achievement of a LDRFME based on a feasible transistor model. A first circuit extends the voltage range of the triode region of an ordinary Junction Field Effect Transistor (JFET). The idea is to use this JFET transistor as a tunable linear resistor. A second memristive non-linear circuit is used to drive the resistance of the first JFET transistor. Then those two circuits are connected together and, under certain conditions, the obtained " resistor” presents a hysteretic behavior, which is considered as a memristive effect. The electrical characteristics of a LDRFME are validated by software simulation and real measurement, respectively.
, Available online  , doi: 10.1109/JAS.2019.1911807 doi: 10.1109/JAS.2019.1911807
Abstract:
A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input delay. The control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error, capable of compensating for input delays. Suitable Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded (GUUB) tracking, provided certain sufficient gain conditions, dependent on the bound of the delay, are satisfied. Simulation results illustrate the performance and robustness of the controller for different values of input delay.
, Available online  , doi: 10.1109/JAS.2019.1911669 doi: 10.1109/JAS.2019.1911669
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This article proposes a new distributed formation flight protocol for unmanned aerial vehicles (UAVs) to perform coordinated circular tracking around a set of circles on a target sphere. Different from the previous results limited in bidirectional networks and disturbance-free motions, this paper handles the circular formation flight control problem with both directed network and spatiotemporal disturbance with the knowledge of its upper bound. Distinguishing from the design of a common Lyaponov function for bidirectional cases, we separately design the control for the circular tracking subsystem and the formation keeping subsystem with the circular tracking error as input. Then the whole control system is regarded as a cascade connection of these two subsystems, which is proved to be stable by input-to-state stability (ISS) theory. For the purpose of encountering the external disturbance, the backstepping technology is introduced to design the control inputs of each UAV pointing to North and Down along the special sphere (say, the circular tracking control algorithm) with the help of the switching function. Meanwhile, the distributed linear consensus protocol integrated with anther switching anti-interference item is developed to construct the control input of each UAV pointing to east along the special sphere (say, the formation keeping control law) for formation keeping. The validity of the proposed control law is proved both in the rigorous theory and through numerical simulations.
, Available online  , doi: 10.1109/JAS.2019.1911729 doi: 10.1109/JAS.2019.1911729
Abstract:
In this paper, a kind of lateral stability control strategy is put forward about the four wheel independent drive electric vehicle. The design of control system adopts hierarchical structure. Unlike the previous control strategy, this paper introduces a method which is the combination of sliding mode control and optimal allocation algorithm. According to the driver's operation commands (steering angle and speed), the steady state responses of the sideslip angle and yaw rate are obtained. Based on this, the reference model is built. Upper controller adopts the sliding mode control principle to obtain the desired yawing moment demand. Lower controller is designed to satisfy the desired yawing moment demand by optimal allocation of the tire longitudinal forces. Firstly, the optimization goal is built to minimize the actuator cost. Secondly, the weighted least-square method is used to design the tire longitudinal forces optimization distribution strategy under the constraint conditions of actuator and the friction oval. Beyond that, when the optimal allocation algorithm is not applied, a method of axial load ratio distribution is adopted. Finally, CarSim associated with Simulink simulation experiments are designed under the conditions of different velocities and different pavements. The simulation results show that the control strategy designed in this paper has a good following effect comparing with the reference model and the sideslip angle \begin{document}$\beta$\end{document} is controlled within a small rang at the same time. Beyond that, based on the optimal distribution mode, the electromagnetic torque phase of each wheel can follow the trend of the vertical force of the tire, which shows the effectiveness of the optimal distribution algorithm.
, Available online  , doi: 10.1109/JAS.2019.1911555 doi: 10.1109/JAS.2019.1911555
Abstract:
For the complex batch process with characteristics of unequal batch data length, a novel data-driven batch process monitoring method is proposed based on mixed data features analysis and multi-way kernel entropy component analysis (MDFA-MKECA) in this paper. Combining the mechanistic knowledge, different mixed data features of each batch including statistical and thermodynamics entropy features, are extracted to finish data pre-processing. After that, MKECA is applied to reduce data dimensionality and finally establish a monitoring model. The proposed method is applied to a reheating furnace industry process, and the experimental results demonstrate that the MDFA-MKECA method can reduce the calculated amount and effectively provide on-line monitoring of the batch process.
, Available online  , doi: 10.1109/JAS.2019.1911726 doi: 10.1109/JAS.2019.1911726
Abstract:
In order to increase the capacity of encrypted information and reduce the loss of information transmission, a three-dimensional scene encryption algorithm based on the phase iteration of the angular spectrum domain is proposed in this paper. The algorithm, which adopts the layer-oriented method, generates the computer generated hologram by encoding the three-dimensional scene. Then the computer generated hologram is encoded into three pure phase functions by adopting the phase iterative algorithm based on angular spectrum domain, and the encryption process is completed. The three-dimensional scene encryption can improve the capacity of the information, and the three-phase iterative algorithm can guarantee the security of the encryption information. The numerical simulation results show that the algorithm proposed in this paper realized the encryption and decryption of three-dimensional scene. At the same time, it can ensure the safety of the encrypted information and increase the capacity of the encrypted information.
, Available online  , doi: 10.1109/JAS.2019.1911639 doi: 10.1109/JAS.2019.1911639
Abstract:
In this paper, a kind of lateral stability control strategy is put forward about the four wheel independent drive electric vehicle. The design of control system adopts hierarchical structure. Unlike the previous control strategy, this paper introduces a method which is the combination of sliding mode control and optimal allocation algorithm. According to the driver's operation commands (steering angle and speed), the steady state responses of the sideslip angle and yaw rate are obtained. Based on this, the reference model is built. Upper controller adopts the sliding mode control principle to obtain the desired yawing moment demand. Lower controller is designed to satisfy the desired yawing moment demand by optimal allocation of the tire longitudinal forces. Firstly, the optimization goal is built to minimize the actuator cost. Secondly, the weighted least-square method is used to design the tire longitudinal forces optimization distribution strategy under the constraint conditions of actuator and the friction oval. Beyond that, when the optimal allocation algorithm is not applied, a method of axial load ratio distribution is adopted. Finally, CarSim associated with Simulink simulation experiments are designed under the conditions of different velocities and different pavements. The simulation results show that the control strategy designed in this paper has a good following effect comparing with the reference model and the sideslip angle \begin{document}$\beta$\end{document} is controlled within a small rang at the same time. Beyond that, based on the optimal distribution mode, the electromagnetic torque phase of each wheel can follow the trend of the vertical force of the tire, which shows the effectiveness of the optimal distribution algorithm.
, Available online  , doi: 10.1109/JAS.2019.1911552 doi: 10.1109/JAS.2019.1911552
Abstract:
Miss distance is a critical parameter of assessing the performance for highly maneuvering targets interception (HMTI). In a realistic terminal guidance system, the control of pursuer \begin{document}$u$\end{document} depends on the estimate of unknown state, thus the miss distance becomes a random variable with a prior unknown distribution. Currently, such a distribution is mainly evaluated by the method of Monte Carlo simulation. In this paper, by integrating the estimation error model of zero-effort miss distance (ZEM) obtained by our previous work, an analytic method for solving the distribution of miss distance is proposed, in which the system is presumed to use a bang-bang control strategy. By comparing with the results of Monte Carlo simulations under four different types of disturbances (maneuvers), the correctness of the proposed method is validated. Results of this paper provide a powerful tool for the design, analysis and performance evaluation of guidance system.
, Available online  , doi: 10.1109/JAS.2019.1911549 doi: 10.1109/JAS.2019.1911549
Abstract:
, Available online  , doi: 10.1109/JAS.2019.1911732 doi: 10.1109/JAS.2019.1911732
Abstract:
Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the " curse of dimensionality” issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network; such a process is called experience replay. Heuristic knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.
, Available online  , doi: 10.1109/JAS.2019.1911531 doi: 10.1109/JAS.2019.1911531
Abstract:
This paper investigates the sliding mode control problem for a class of discrete-time nonlinear networked Markovian jump systems in the presence of probabilistic Denial-of-Service attacks. The communication network via which the data is propagated is unsafe and the malicious adversary can attack the system during state feedback. By considering random Denial-of-Service attacks, a new sliding mode variable is designed, which takes into account the distribution information of the probabilistic attacks. Then, by resorting to Lyapunov theory and stochastic analysis methods, sufficient conditions are established for the existence of the desired sliding mode controller, guaranteeing both reachability of the designed sliding surface and stability of the resulting sliding motion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed sliding mode control algorithm.
, Available online  , doi: 10.1109/JAS.2019.1911651 doi: 10.1109/JAS.2019.1911651
Abstract:
Networked control systems are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices, increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems, process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.
, Available online
Abstract:
Based on ACP (Artificial Systems, Computing Experiments, and Parallel Execution) methodology, parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems. This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework, core technologies, and the application demonstration. The future research, application directions, and suggestions are also discussed.
, Available online  , doi: 10.1109/JAS.2019.1911720 doi: 10.1109/JAS.2019.1911720
Abstract:
In a passive ultra-high frequency (UHF) radio frequency identification (RFID) system, the recovery of collided tag signals on a physical layer can enhance identification efficiency. However, frequency drift is very common in UHF RFID systems, and will have an influence on the recovery on the physical layer. To address the problem of recovery with the frequency drift, this paper adopts a radial basis function (RBF) network to separate the collision signals, and decode the signals via FM0 to recovery collided RFID tags. Numerical results show that the method in this paper has better performance of symbol error rate (SER) and separation efficiency compared to conventional methods when frequency drift occurs.
, Available online  , doi: 10.1109/JAS.2019.1911663 doi: 10.1109/JAS.2019.1911663
Abstract:
In this paper, an open-loop PD-type iterative learning control (ILC) scheme is first proposed for two kinds of distributed parameter systems (DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and \begin{document}$m$\end{document} actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained in the sense of the \begin{document}$\lambda$\end{document}-norm. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.
, Available online  , doi: 10.1109/JAS.2019.1911528 doi: 10.1109/JAS.2019.1911528
Abstract:
Structure reconstruction of 3D anatomy from bi-planar X-ray images is a challenging topic. Traditionally, the elastic-model-based method was used to reconstruct 3D shapes by deforming the control points on the elastic mesh. However, the reconstructed shape is not smooth because the limited control points are only distributed on the edge of the elastic mesh. Alternatively, statistical-model-based methods, which include shape-model-based and intensity-model-based methods, are introduced due to their smooth reconstruction. However, both suffer from limitations. With the shape-model-based method, only the boundary profile is considered, leading to the loss of valid intensity information. For the intensity-based-method, the computation speed is slow because it needs to calculate the intensity distribution in each iteration. To address these issues, we propose a new reconstruction method using X-ray images and a specimen’s CT data. Specifically, the CT data provides both the shape mesh and the intensity model of the vertebra. Intensity model is used to generate the deformation field from X-ray images, while the shape model is used to generate the patient specific model by applying the calculated deformation field. Experiments on the public synthetic dataset and clinical dataset show that the average reconstruction errors are 1.1 mm and 1.2 mm, separately. The average reconstruction time is 3 minutes.
, Available online  , doi: 10.1109/JAS.2019.1911636 doi: 10.1109/JAS.2019.1911636
Abstract:
The Möller algorithm is a self-stabilizing minor component analysis algorithm. This research document involves the study of the convergence and dynamic characteristics of the Möller algorithm using the deterministic discrete time (DDT) methodology. Unlike other analysis methodologies, the DDT methodology is capable of serving the distinct time characteristic and having no constraint conditions. Through analyzing the dynamic characteristics of the weight vector, several convergence conditions are drawn, which are beneficial for its application. The performing computer simulations and real applications demonstrate the correctness of the analysis’s conclusions.
, Available online  , doi: 10.1109/JAS.2019.1911534 doi: 10.1109/JAS.2019.1911534
Abstract:
Hand gesture recognition is a popular topic in computer vision and makes human-computer interaction more flexible and convenient. The representation of hand gestures is critical for recognition. In this paper, we propose a new method to measure the similarity between hand gestures and exploit it for hand gesture recognition. The depth maps of hand gestures captured via the Kinect sensors are used in our method, where the 3D hand shapes can be segmented from the cluttered backgrounds. To extract the pattern of salient 3D shape features, we propose a new descriptor–3D Shape Context, for 3D hand gesture representation. The 3D Shape Context information of each 3D point is obtained in multiple scales because both local shape context and global shape distribution are necessary for recognition. The description of all the 3D points constructs the hand gesture representation, and hand gesture recognition is explored via dynamic time warping algorithm. Extensive experiments are conducted on multiple benchmark datasets. The experimental results verify that the proposed method is robust to noise, articulated variations, and rigid transformations. Our method outperforms state-of-the-art methods in the comparisons of accuracy and efficiency.
, Available online  , doi: 10.1109/JAS.2019.1911648 doi: 10.1109/JAS.2019.1911648
Abstract:
The passwords for unlocking the mobile devices are relatively simple, easier to be stolen, which causes serious potential security problems. An important research direction of identity authentication is to establish user behavior models to authenticate users. In this paper, a mobile terminal APP browsing behavioral authentication system architecture which synthesizes multiple factors is designed. This architecture is suitable for users using the mobile terminal APP in the daily life. The architecture includes data acquisition, data processing, feature extraction, and sub model training. We can use this architecture for continuous authentication when the user uses APP at the mobile terminal.
, Available online  , doi: 10.1109/JAS.2019.1911519 doi: 10.1109/JAS.2019.1911519
Abstract:
This paper is concerned with a control problem of a diffusion process with the help of static mesh sensor networks in a certain region of interest and a team of networked mobile actuators carrying chemical neutralizers. The major contribution of this paper can be divided into three parts: the first is the construction of a cyber-physical system framework based on Centroidal Voronoi Tessellations (CVTs), the second is the convergence analysis of the actuators location, and the last is a novel proportional integral (PI) control method for actuator motion planning and neutralizing control (e.g. spraying) of a diffusion process with a moving or static pollution source, which is more effective than a proportional (P) control method. An optimal spraying control cost function is constructed. Then, the minimization problem of the spraying amount is addressed. Moreover, a new CVT algorithm based on the novel PI control method, henceforth called PI-CVT algorithm, is introduced together with the convergence analysis of the actuators location via a PI control law. Finally, a modified simulation platform called Diffusion-Mobile-Actuators-Sensors-2-Dimension-Proportional Integral Derivative (Diff-MAS2D-PID) is illustrated. In addition, a numerical simulation example for the diffusion process is presented to verify the effectiveness of our proposed controllers.
, Available online  , doi: 10.1109/JAS.2019.1911627 doi: 10.1109/JAS.2019.1911627
Abstract:
The stabilization problem of distributed proportional-integral-derivative (PID) controllers for general first-order multi-agent systems with time delay is investigated in the paper. The closed-loop multi-input multi-output (MIMO) framework in frequency domain is firstly introduced for the multi-agent system. Based on the matrix theory, the whole system is decoupled into several subsystems with respect to the eigenvalues of the Laplacian matrix. Considering that the eigenvalues may be complex numbers, the consensus problem of the multi-agent system is transformed into the stabilizing problem of all the subsystems with complex coefficients. For each subsystem with complex coefficients, the range of admissible proportional gains \begin{document}${k_{\rm{P}}}$\end{document} is analytically determined. Then, the stabilizing region in the space of integral gain (\begin{document}${k_{\rm{I}}}$\end{document}) and derivative gain (\begin{document}${k_{\rm{D}}}$\end{document}) for a given \begin{document}${k_{\rm{P}}}$\end{document} value is also obtained in an analytical form. The entire stabilizing set can be determined by sweeping \begin{document}${k_{\rm{P}}}$\end{document} in the allowable range. The proposed method is conducted for general first-order multi-agent systems under arbitrary topology including undirected and directed graph topology. Besides, the results in the paper provide the basis for the design of distributed PID controllers satisfying different performance criteria. The simulation examples are presented to check the validity of the proposed control strategy
, Available online  , doi: 10.1109/JAS.2019.1911624 doi: 10.1109/JAS.2019.1911624
Abstract:
This paper investigates the stability of switched systems with time-varying delay and all unstable subsystems. According to the stable convex combination, we design a state-dependent switching rule. By employing wirtinger integral inequality and Leibniz-Newton formula, the stability results of nonlinear delayed switched systems whose nonlinear terms satisfy Lipschitz condition under the designed state-dependent switching rule are established for different assumptions on time delay. Moreover, some new stability results for linear delayed switched systems are also presented. The effectiveness of the proposed results is validated by two typical numerical examples.
, Available online  , doi: 10.1109/JAS.2019.1911633 doi: 10.1109/JAS.2019.1911633
Abstract:
As a complex and critical cyber-physical system (CPS), the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy (EMS) is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory (LSTM) network based parallel reinforcement learning (PRL) approach to construct EMS for a hybrid tracked vehicle (HTV). This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning (RL) framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.
, Available online  , doi: 10.1109/JAS.2019.1911522 doi: 10.1109/JAS.2019.1911522
Abstract:
Linguistic single-valued neutrosophic set (LSVNS) is a more reliable tool, which is designed to handle the uncertainties of the situations involving the qualitative data. In the present manuscript, we introduce some power aggregation operators (AOs) for the LSVNSs, whose purpose is to diminish the influence of inevitable arguments about the decision-making process. For it, first we develop some averaging power operators, namely, linguistic single-valued neutrosophic (LSVN) power averaging, weighted average, ordered weighted average, and hybrid averaging AOs along with their desirable properties. Further, we extend it to the geometric power AOs for LSVNSs. Based on the proposed work; an approach to solve the group decision-making problems is given along with the numerical example. Finally, a comparative study and the validity tests are present to discuss the reliability of the proposed operators.
, Available online  , doi: 10.1109/JAS.2019.1911645 doi: 10.1109/JAS.2019.1911645
Abstract:
Prediction intervals (PIs) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks (KDBN), serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference. This study proposes a variational inference method for the KDBN for the purpose of fast inference, which avoids the time-consuming stochastic sampling. The proposed algorithm contains two stages. The first stage involves the inference of the missing inputs by using a local linearization based variational inference, and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices. To verify the effectiveness of the proposed method, a synthetic dataset and a practical dataset of generation flow of blast furnace gas (BFG) are employed with different ratios of missing inputs. The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one.
, Available online  , doi: 10.1109/JAS.2019.1911642 doi: 10.1109/JAS.2019.1911642
Abstract:
The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection. Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components. This paper proposes flue gas monitoring system with empirically-trained dictionary (ETD) to deal with the complexity and biases brought by the uninformative spectral data. Firstly, ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation. D1, D2 and D3 are three types of ETD obtained by different methods. Then, the predictive model of component concentration is constructed on the ETD. In the experiments, two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares, the background propagation neural network and the support vector machines are performed. Moreover, the optimal parameters are chosen according to the 10-fold rootmean-square error of cross validation. The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems. Note to Practitioners—The monitoring of the flue gas of the thermal power plants is very important and spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components. However, the redundant and unrelated information of spectra lower the precision of the predictive model. This paper proposes flue gas monitoring system with ETD to deal with the complexity and biases brought by the uninformative spectral data. The predictive model of component concentration is constructed on the ETD instead of the original spectral space. The proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.
, Available online  , doi: 10.1109/JAS.2019.1911738 doi: 10.1109/JAS.2019.1911738
Abstract:
In networked robot manipulators that deeply integrate control, communication and computation, the controller design needs to take into consideration the limited or costly system resources and the presence of disturbances/uncertainties. To cope with these requirements, this paper proposes a novel dynamic event-triggered robust tracking control method for a one-degree of freedom (DOF) link manipulator with external disturbance and system uncertainties via a reduced-order generalized proportional-integral observer (GPIO). By only using the sampled-data position signal, a new sampled-data robust output feedback tracking controller is proposed based on a reduced-order GPIO to attenuate the undesirable influence of the external disturbance and the system uncertainties. To save the communication resources, we propose a discrete-time dynamic event-triggering mechanism (DETM), where the estimates and the control signal are transmitted and computed only when the proposed discrete-time DETM is violated. It is shown that with the proposed control method, both tracking control properties and communication properties can be significantly improved. Finally, simulation results are shown to demonstrate the feasibility and efficacy of the proposed control approach.
, Available online  , doi: 10.1109/JAS.2019.1911813 doi: 10.1109/JAS.2019.1911813
Abstract:
In this paper, we focus on the scenario of using unmanned aerial vehicles base stations (UAV-BSs) to serve cellular users. In particular, we focus on finding the minimum number of UAV-BSs as well as their deployment. We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the UE covered ratio is no less than the expectation of network suppliers, the UEs receive acceptable downlink rates, and the UAV-BSs can work in a sustainable manner. We show the NP-hardness of this problem and then propose a method to address it. The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio, given the number of UAV-BSs within that range. We present a maximizing algorithm to solve it with the proof of convergence. Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.
, Available online
Abstract:
This paper focuses on facilitating state-of-the-art applications of Big Data Analytics (BDA) architectures and infrastructures to telecommunications (telecom) industrial sector. Telecom companies are dealing with terabytes to petabytes of data on a daily basis. IoT applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts (POC) on a severely limited BDA technology stack (as compared to the available technology stack), i.e., we didn’t find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation (called LambdaTel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines. We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe LambdaTel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.
, Available online
Abstract:
This paper presents an experimental study to compare the performance of model-free control strategies for pneumatic soft robots. Fabricated using soft materials, soft robots have gained much attention in academia and industry during recent years because of their inherent safety in human interaction. However, due to structural flexibility and compliance, mathematical models for these soft robots are nonlinear with an infinite degree of freedom (DOF). Therefore, accurate position (or orientation) control and optimization of their dynamic response remains a challenging task. Most existing soft robots currently employed in industrial and rehabilitation applications use model-free control algorithms such as PID. However, to the best of our knowledge, there has been no systematic study on the comparative performance of model-free control algorithms and their ability to optimize dynamic response, i.e., reduce overshoot and settling time. In this paper, we present comparative performance of several variants of model-free PID-controllers based on extensive experimental results. Additionally, most of the existing work on model-free control in pneumatic soft-robotic literature use manually tuned parameters, which is a time-consuming, labor-intensive task. We present a heuristic-based coordinate descent algorithm to tune the controller parameter automatically. We presented results for both manual tuning and automatic tuning using the Ziegler–Nichols method and proposed algorithm, respectively. We then used experimental results to statistically demonstrate that the presented automatic tuning algorithm results in high accuracy. The experiment results show that for soft robots, the PID-controller essentially reduces to the PI controller. This behavior was observed in both manual and automatic tuning experiments; we also discussed a rationale for removing the derivative term.
, Available online
Abstract:
This paper proposes a tube-based method for the asynchronous observation problem of discrete-time switched linear systems in the presence of amplitude-bounded disturbances. Sufficient stability conditions of the nominal observer error system under mode-dependent persistent dwell-time (MPDT) switching are first established. Taking the disturbances into account, a novel asynchronous MPDT robust positive invariant (RPI) set and an asynchronous MPDT generalized RPI (GRPI) set are determined for the difference system between the nominal and disturbed observer error systems. Further, the global uniform asymptotical stability of the observer error system is established in the sense of converging to the asynchronous MPDT GRPI set, i.e., the cross section of the tube of the observer error system. Finally, the proposed results are validated on a space robot manipulator example.
, Available online
Abstract:
In many traditional non-rigid structure from motion (NRSFM) approaches, the estimation results of part feature points may significantly deviate from their true values because only the overall estimation error is considered in their models. Aimed at solving this issue, a local deviation-constrained-based column-space-fitting approach is proposed in this paper to alleviate estimation deviation. In our work, an effective model is first constructed with two terms: the overall estimation error, which is computed by a linear subspace representation, and a constraint term, which is based on the variance of the reconstruction error for each frame. Furthermore, an Augmented Lagrange Multipliers (ALM) iterative algorithm is presented to optimize the proposed model. Moreover, a convergence analysis is performed with three steps for the optimization process. As both the overall estimation error and the local deviation are utilized, the proposed method can achieve a good estimation performance and a relatively uniform estimation error distribution for different feature points. Experimental results on several widely used synthetic sequences and real sequences demonstrate the effectiveness and feasibility of the proposed algorithm.
, Available online
Abstract:
In this paper, an adaptive backstepping control scheme is proposed for attitude tracking of non-rigid spacecraft in the presence of input quantization, inertial uncertainty and external disturbance. The control signal for each actuator is quantized by sector-bounded quantizers, including the logarithmic quantizer and the hysteresis quantizer. By describing the impact of quantization in a new affine model and introducing a smooth function and a novel form of the control signal, the influence caused by input quantization and external disturbance is properly compensated for. Moreover, with the aid of the adaptive control technique, our approach can achieve attitude tracking without the explicit knowledge of inertial parameters. Unlike existing attitude control schemes for spacecraft, in this paper, the quantization parameters can be unknown, and the bounds of inertial parameters and disturbance are also not needed. In addition to proving the stability of the closed-loop system, the relationship between the control performance and design parameters is analyzed. Simulation results are presented to illustrate the effectiveness of the proposed scheme.
, Available online
Abstract:
The phenomenon of mixed-mode is one of the most important characteristic of a switched delay system. If an networked control system (NCS) with network induced delays and packed dropouts (NIDs & PDs) is recast as a switched delay system, considering the effect of mixed-modes to the stability analysis is essential for an NCS. In this paper, with the help of interpolatory quadrature formula and by the average dwell time method, stabilization of NCSs using a mixed-mode based switched delay system method is investigated based on a novel constructed Lyapunov-Krasovskii functional. With Finsler's Lemma, new exponential stabilizability conditions with less conservativeness are given for the NCS. Finally, an illustrative example is provided to verify the effectiveness of the developed results.

IEEE/CAA Journal of Automatica Sinica

• CiteScore 2018: 5.31
Rank：Top 9% (Category of Control and Systems Engineering), Top 10% (Categories of Information System and Artificial Intelligence)