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High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach
Shigen Gao, Yuhan Hou, Hairong Dong, Sebastian Stichel, Bin Ning
, Available online  , doi: 10.1109/JAS.2019.1911582 doi: 10.1109/JAS.2019.1911582
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This paper addresses the control design for automatic train operation of high-speed trains with protection constraints. A new resilient nonlinear gain-based feedback control approach is proposed, which is capable of guaranteeing, under some proper non-restrictive initial conditions, the protection constraints control raised by the distance-to-go (moving authority) curve and automatic train protection in practice. A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains. The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability, no adaptations of unknown parameters, function approximation of unknown nonlinearities, and attenuation of external disturbances in the proposed control strategies. Finally, rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.
On Cost Aware Cloudlet Placement for Mobile Edge Computing
Qiang Fan, Nirwan Ansari
, Available online  , doi: 10.1109/JAS.2019.1911564 doi: 10.1109/JAS.2019.1911564
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As accessing computing resources from the remote cloud inherently incurs high end-to-end (E2E) delay for mobile users, cloudlets, which are deployed at the edge of a network, can potentially mitigate this problem. Although some research works focus on allocating workloads among cloudlets, the cloudlet placement aiming to minimize the deployment cost (i.e., consisting of both the cloudlet cost and average E2E delay cost) has not been addressed effectively so far. The locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of users. Therefore, in this paper, we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing (CAPABLE) strategy, where both the cloudlet cost and average E2E delay are considered in the cloudlet placement. To solve this problem, a Lagrangian heuristic algorithm is developed to achieve the suboptimal solution. After cloudlets are placed in the network, we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user mobility. The performance of CAPABLE has been validated by extensive simulations.
A Mixed-Depth Visual Rendering Method for Bleeding Simulation
Wen Shi, Peter Xiaoping Liu, Minhua Zheng
, Available online  , doi: 10.1109/JAS.2019.1911561 doi: 10.1109/JAS.2019.1911561
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The visual fidelity of bleeding simulation in a surgical simulator is critical since it will affect not only the degree of visual realism, but also the user’s medical judgment and treatment in real-life settings. The conventional marching cubes surface rendering algorithm provides excellent visual effect in rendering gushing blood, however, it is insufficient for blood flow, which is very common in surgical procedures, since in this case the rendered surface and depth textures of blood are rough. In this paper, we propose a new method called the mixed depth rendering for rendering blood flow in surgical simulation. A smooth height field is created to minimize the height difference between neighboring particles on the bleeding surface. The color and transparency of each bleeding area are determined by the number of bleeding particles, which is consistent with the real visual effect. In addition, there is no much extra computational cost. The rendering of blood flow in a variety of surgical scenarios shows that visual feedback is much improved. The proposed mixed depth rendering method is also used in a neurosurgery simulator that we developed.
Integrated Design and Accuracy Analysis of Star Sensor and Gyro on the Same Benchmark for Satellite Attitude Determination System
Bowen Hou, Zhangming He, Haiyin Zhou, Jiongqi Wang
, Available online  , doi: 10.1109/JAS.2019.1911600 doi: 10.1109/JAS.2019.1911600
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As an important sensor in the navigation systems, star sensors and the gyro play important roles in spacecraft attitude determination system. Complex environmental factors are the main sources of error in attitude determination. The error influence of different benchmarks and the disintegration mode between the star sensor and the gyro is analyzed in theory. The integrated design of the star sensor and the gyro on the same benchmark can effectively avoid the error influence and improves the spacecraft attitude determination accuracy. Simulation results indicate that when the stars sensor optical axis vectors overlap the reference coordinate axis of the gyro in the same benchmark, the attitude determination accuracy improves.
Balance Control of a Biped Robot on a Rotating Platform Based on Efficient Reinforcement Learning
Ao Xi, Thushal Wijekoon Mudiyanselage, Dacheng Tao, Chao Chen
, Available online  , doi: 10.1109/JAS.2019.1911567 doi: 10.1109/JAS.2019.1911567
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In this work, we combined the model based reinforcement learning (MBRL) and model free reinforcement learning (MFRL) to stabilize a biped robot (NAO robot) on a rotating platform, where the angular velocity of the platform is unknown for the proposed learning algorithm and treated as the external disturbance. Nonparametric Gaussian processes normally require a large number of training data points to deal with the discontinuity of the estimated model. Although some improved method such as probabilistic inference for learning control (PILCO) does not require an explicit global model as the actions are obtained by directly searching the policy space, the overfitting and lack of model complexity may still result in a large deviation between the prediction and the real system. Besides, none of these approaches consider the data error and measurement noise during the training process and test process, respectively. We propose a hierarchical Gaussian processes (GP) models, containing two layers of independent GPs, where the physically continuous probability transition model of the robot is obtained. Due to the physically continuous estimation, the algorithm overcomes the overfitting problem with a guaranteed model complexity, and the number of training data is also reduced. The policy for any given initial state is generated automatically by minimizing the expected cost according to the predefined cost function and the obtained probability distribution of the state. Furthermore, a novel Q(λ). based MFRL method scheme is employed to improve the policy. Simulation results show that the proposed RL algorithm is able to balance NAO robot on a rotating platform, and it is capable of adapting to the platform with varying angular velocity.
Image Analysis by Two Types of Franklin-Fourier Moments
Bing He, Jiangtao Cui, Bin Xiao, Xuan Wang
, Available online  , doi: 10.1109/JAS.2019.1911591 doi: 10.1109/JAS.2019.1911591
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In this paper, we first derive two types of transformed Franklin polynomial: substituted and weighted radial Franklin polynomials. Two radial orthogonal moments are proposed based on these two types of polynomials, namely substituted Franklin-Fourier moments and weighted Franklin-Fourier moments (SFFMs and WFFMs), which are orthogonal in polar coordinates. The radial kernel functions of SFFMs and WFFMs are transformed Franklin functions and Franklin functions are composed of a class of complete orthogonal splines function system of degree one. Therefore, it provides the possibility of avoiding calculating high order polynomials, and thus the accurate values of SFFMs and WFFMs can be obtained directly with little computational cost. Theoretical and experimental results show that Franklin functions are not well suited for constructing higher-order moments of SFFMs and WFFMs, but compared with traditional orthogonal moments (e.g., BFMs, OFMs and ZMs) in polar coordinates, the proposed two types of Franklin-Fourier Moments have better performance respectively in lower-order moments.
A Correntropy-based Affine Iterative Closest Point Algorithm for Robust Point Set Registration
Hongchen Chen, Xie Zhang, Shaoyi Du, Zongze Wu, Nanning Zheng
, Available online  , doi: 10.1109/JAS.2019.1911579 doi: 10.1109/JAS.2019.1911579
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The iterative closest point (ICP) algorithm has the advantages of high accuracy and fast speed for point set registration, but it performs poorly when the point set has a large number of noisy outliers. To solve this problem, we propose a new affine registration algorithm based on correntropy which works well in the affine registration of point sets with outliers. Firstly, we substitute the traditional measure of least squares with a maximum correntropy criterion to build a new registration model, which can avoid the influence of outliers. To maximize the objective function, we then propose a robust affine ICP algorithm. At each iteration of this new algorithm, we set up the index mapping of two point sets according to the known transformation, and then compute the closed-form solution of the new transformation according to the known index mapping. Similar to the traditional ICP algorithm, our algorithm converges to a local maximum monotonously for any given initial value. Finally, the robustness and high efficiency of affine ICP algorithm based on correntropy are demonstrated by 2D and 3D point set registration experiments.
An Overview and Perspectives On Bidirectional Intelligence: Lmser Duality, Double Ia Harmony, and Causal Computation
Lei Xu
, Available online  
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Advances on bidirectional intelligence are overviewed along three threads, with extensions and new perspectives. The first thread is about bidirectional learning architecture, exploring five dualities that enable Lmser six cognitive functions and provide new perspectives on which a lots of extensions and particularlly flexible Lmser are proposed. Interestingly, either or two of these dualities actually take an important role in recent models such as U-net, ResNet, and DenseNet. The second thread is about bidirectional learning principles unified by best yIng-yAng (IA) harmony in BYY system. After getting insights on deep bidirectional learning from a bird-viewing on existing typical learning principles from one or both of the inward and outward directions, maximum likelihood, variational principle, and several other learning principles are summarised as exemplars of the BYY learning, with new perspectives on advanced topics. The third thread further proceeds to deep bidirectional intelligence, driven by long term dynamics (LTD) for parameter learning and short term dynamics (STD) for image thinking and rational thinking in harmony. Image thinking deals with information flow of continuously valued arrays and especially image sequence, as if thinking was displayed in the real world, exemplified by the flow from inward encoding / cognition to outward reconstruction / transformation in Lmser learning and BYY learning. In contrast, rational thinking handles symbolic strings or discretely valued vectors, performing uncertainty reasoning and problem solving. In particular, a general thesis is proposed for bidirectional intelligence, featured by BYY intelligence potential theory (BYY-IPT) and nine essential dualities in architecture, fundamentals, and implementation, respectively. Then, problems of combinatorial solving and uncertainty reasoning are investigated from this BYY IPT perspective. First, variants and extensions are suggested for AlphaGoZero like searching. Also, traveling salesman problem (TSP) and attributed graph matching (AGM) are turned into Go like problems via a feature enrichment technique. Second, reasoning activities are summarised under guidance of BYY IPT from the aspects of constraint satisfaction, uncertainty propagation, and path or tree searching. Particularly, causal potential theory is proposed for discovering causal direction, with two roads developed for its implementation.
Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems
Adedapo Odekunle, Weinan Gao, Yebin Wang
, Available online  
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In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is assured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.
Optimal Valve Closure Operations for Pressure Suppression in Fluid Transport Pipelines
Tehuan Chen, Zhigang Ren
, Available online  , doi: 10.1109/JAS.2019.1911585 doi: 10.1109/JAS.2019.1911585
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When a valve is suddenly closed in fluid transport pipelines, a pressure surge or shock is created along the pipeline due to the momentum change. This phenomenon, called hydraulic shock, can cause major damage to the pipelines. In this paper, we introduce a hyperbolic partial differential equation (PDE) system to describe the fluid flow in the pipeline and propose an optimal boundary control problem for pressure suppression during the valve closure. The boundary control in this system is related to the valve actuation located at the pipeline terminus through a valve closing model. To solve this optimal boundary control problem, we use the method of lines and orthogonal collocation to obtain a spatial-temporal discretization model based on the original pipeline transmission PDE system. Then, the optimal boundary control problem is reduced to a nonlinear programming (NLP) problem that can be solved using nonlinear optimization techniques such as sequential quadratic programming (SQP). Finally, we conclude the paper with simulation results demonstrating that the full parameterization (FP) method eliminates pressure shock effectively and costs less computation time compared with the control vector parameterization (CVP) method.
Stabilization of the Cascaded ODE-Schrödinger Equations Subject to Observation With Time Delay
Aye Aye Than, Junmin Wang
, Available online  , doi: 10.1109/JAS.2019.1911588 doi: 10.1109/JAS.2019.1911588
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This paper focuses on the stabilization of the cascaded Schrödinger-ODE equations subject to the observation with time delay. Both observer and predictor systems are designed to estimate the state variable on the time interval \begin{document}$[0, t-\tau]$\end{document} when the observation is available, and to predict the state variable on the time interval \begin{document}$[t-\tau, t]$\end{document} when the observation is not available, respectively. Based on the estimated state variable and the output feedback stabilizing controller using the backstepping method, it is shown that the closed-loop system is exponentially stable.
A Survey of Multi-robot Regular and Adversarial Patrolling
Li Huang, MengChu Zhou, Kuangrong Hao, Edwin Hou
, Available online  , doi: 10.1109/JAS.2019.1911537 doi: 10.1109/JAS.2019.1911537
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Multi-robot systems can be applied to patrol a concerned environment for security purposes. According to different goals, this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling. Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed, while adversarial one focuses on unpredictable robots’ moving patterns to maximize adversary detection probability. Under each category, a systematic survey is done including problem statements and modeling, patrolling objectives and evaluation criteria, and representative patrolling strategies and approaches. Existing problems and open questions are presented accordingly.
A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories
Xiuqin Shang, Dayong Shen, Fei-Yue Wang, Timo R. Nyberg
, Available online  
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We study the fabric spreading and cutting problem in apparel factories. For the sake of saving the material costs, the cutting requirement should be met exactly without producing additional garment components. For reducing the production costs, the number of lays that corresponds to the frequency of using the cutting beds should be minimized. We propose an iterated greedy algorithm for solving the fabric spreading and cutting problem. This algorithm contains a constructive procedure and an improving loop. Firstly the constructive procedure creates a set of lays in sequence, and then the improving loop tries to pick each lay from the lay set and rearrange the remaining lays into a smaller lay set. The improving loop will run until it cannot obtain any smaller lay set or the time limit is due. The experiment results on 500 cases show that the proposed algorithm is effective and efficient.
Consensus Control With a Constant Gain for Discrete-time Binary-valued Multi-agent Systems Based on a Projected Empirical Measure Method
Ting Wang, Min Hu, Yanlong Zhao
, Available online  
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This paper studies the consensus control of multiagent systems with binary-valued observations. An algorithm alternating estimation and control is proposed. Each agent estimates the states of its neighbors based on a projected empirical measure method for a holding time. Based on the estimates, each agent designs the consensus control with a constant gain at some skipping time. The states of the system are updated by the designed control, and the estimation and control design will be repeated. For the estimation, the projected empirical measure method is proposed for the binary-valued observations. The algorithm can ensure the uniform boundedness of the estimates and the mean square error of the estimation is proved to be at the order of the reciprocal of the holding time (the same order as that in the case of accurate outputs). For the consensus control, a constant gain is designed instead of the stochastic approximation based gain in the existing literature for binary-valued observations. And, there is no need to make modification for control since the uniform boundedness of the estimates ensures the uniform boundedness of the agents’ states. Finally, the systems updated by the designed control are proved to achieve consensus and the consensus speed is faster than that in the existing literature. Simulations are given to demonstrate the theoretical results.
A Simulation Engine for Stochastic Timed Petri Nets and Application to Emergency Healthcare Systems
Jiani Zhou, Jiacun Wang, Jun Wang
, Available online  , doi: 10.1109/JAS.2019.1911576 doi: 10.1109/JAS.2019.1911576
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In many service delivery systems, the quantity of available resources is often a decisive factor of service quality. Resources can be personnel, offices, devices, supplies, and so on, depending on the nature of the services a system provides. Although service computing has been an active research topic for decades, general approaches that assess the impact of resource provisioning on service quality matrices in a rigorous way remain to be seen. Petri nets have been a popular formalism for modeling systems exhibiting behaviors of competition and concurrency for almost a half century. Stochastic timed Petri nets (STPN), an extension to regular Petri nets, are a powerful tool for system performance evaluation. However, we did not find any single existing STPN software tool that supports all timed transition firing policies and server types, not to mention resource provisioning and requirement analysis. This paper presents a generic and resource oriented STPN simulation engine that provides all critical features necessary for the analysis of service delivery system quality vs. resource provisioning. The power of the simulation system is illustrated by an application to emergency health care systems.
Event-Triggered Sliding Mode Control for Trajectory Tracking of Nonlinear Systems
Aquib Mustafa, Narendra K. Dhar, Nishchal K Verma
, Available online  
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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.
Indoor INS/UWB-based Human Localization With Missing Data Utilizing Predictive UFIR Filtering
Yuan Xu, Choon Ki Ahn, Yuriy S. Shmaliy, Xiyuan Chen, Lili Bu
, Available online  
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A combined algorithm for the loosely fused ultra wide band (UWB) and inertial navigation system (INS)-based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS/UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response (UFIR) fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.
Stability of delayed switched systems with state-dependent switching
Chao Liu, Zheng Yang, Xiaoyang Liu, Xianying Huang
, Available online  
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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.
A Review on Swarm Intelligence and Evolutionary Algorithms for Solving Flexible Job Shop Scheduling Problems
Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan
, Available online  
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Flexible Job Shop Scheduling Problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population and local search operators for improving convergence performance are summarized. Next, one classical hybrid genetic algorithm (GA) and one newest imperialist competitive algorithm (ICA) with variables neighborhood search (VNS) for solving FJSP are presented. Finally, we summarize, discus and analyze the status of SI and EA for solving FJSP and give insight into future research directions.
Vision Based Hand Gesture Recognition Using 3D Shape Context
Chen Zhu, Jianyu Yang, Zhanpeng Shao, Chunping Liu
, Available online  , doi: 10.1109/JAS.2019.1911534 doi: 10.1109/JAS.2019.1911534
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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.
A Novel MDFA-MKECA Method with Application to Industrial Batch Process Monitoring
Yinghua Yang, Xiang Shi, Xiaozhi Liu, Honghu Li
, Available online  
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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.
A Delay-Dependent Anti-Windup Compensator for Wide-Area Power Systems with Time-Varying Delays and Actuator Saturation
Maddela Chinna Obaiah, Bidyadhar Subudhi
, Available online  
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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.
A new fire detection method using a multi-expert system based on color dispersion, similarity and centroid motion in indoor environment
Teng Wang, Leping Bu, Zhikai Yang, Peng Yuan, Lei Shi
, Available online  
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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.
Three-dimensional Scene Encryption Algorithm Based on Phase Iteration Algorithm of the Angular-Spectral Domain
Chao Han, Yuzhen Shen
, Available online  
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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.
Resilient Fixed-order Distributed Dynamic Output Feedback Load Frequency Control Design for Interconnected Multi-area Power Systems
Ali Azarbahram, Amir Amini, Mahdi Sojoodi
, Available online  
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The paper proposes a novel \begin{document}$ H_\infty$\end{document} load frequency control (LFC) design method for multi-area power systems based on an integral-based non-fragile distributed fixed-order dynamic output feedback (DOF) tracking-regulator control scheme. To this end, we consider a nonlinear interconnected model for multi-area power systems which also include uncertainties and time-varying communication delays. The design procedure is formulated using semi-definite programming and linear matrix inequality (LMI) method. The solution of the proposed LMIs returns necessary parameters for the tracking controllers such that the impact of model uncertainty and load disturbances are minimized. The proposed controllers are capable of receiving all or part of subsystems information, whereas the outputs of each controller are local. These controllers are designed such that the resilient stability of the overall closed-loop system is guaranteed. Simulation results are provided to verify the effectiveness of the proposed scheme. Simulation results quantify that the distributed (and decentralized) controlled system behaves well in presence of large parameter perturbations and random disturbances on the power system.
Four Wheel Independent Drive Electric Vehicle Lateral Stability Control Strategy
Yantao Tian, Xuanhao Cao, Xiaoyu Wang, Yanbo Zhao
, Available online  
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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 $\end{document} is controlled within a small rang at the same time. Beyond that, based on the optimal distribution mode, the electromagnetic torque phase of each wheel can follow the trend of the vertical force of the tire, which shows the effectiveness of the optimal distribution algorithm.
Distribution of Miss Distance for Pursuit-Evasion Problem
Shengwen Xiang, Hongqi Fan, Qiang Fu
, Available online  
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Miss distance is a critical parameter of assessing the performance for highly maneuvering targets interception (HMTI). In a realistic terminal guidance system, the control of pursuer \begin{document}$ 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.
Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints with Preload Forces
Gaofeng Li, Dezhen Song, Lei Sun, Shan Xu, Hongpeng Wang, Jingtai Liu
, Available online  
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To balance the contradiction between higher flexibility and heavier load bearing capacity, we present a novel deformable manipulator which is composed of active rigid joints and deformable links. The deformable link is composed of passive spherical joints with preload forces between socket-ball surfaces. To estimate the load bearing capacity of a deformable link, we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable link. This yields an important result that the load bearing capacity of a spherical joint only depends on its radius, preload force, and static friction coefficient. We further develop a parameter estimation method to estimate the product of preload force and static friction coefficient. The experimental results validate our model. 80.4% of percentage errors on the maximum payload mass prediction are below 15%.
Path Planning for Intelligent Robots Based on Deep Q-learning with Experience Replay and Heuristic Knowledge
Lan Jiang, Hongyun Huang, Zuohua Ding
, Available online  
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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.
Sliding Mode Control for Nonlinear Markovian Jump Systems under Denial-of-Service Attacks
Lei Liu, Lifeng Ma, Yiwen Wang, Jie Zhang, Yuming Bo
, Available online  , doi: 10.1109/JAS.2019.1911531 doi: 10.1109/JAS.2019.1911531
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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.
Research Progress of Parallel Control and Management
Gang Xiong, Xisong Dong, Feiyue Wang, Hao Lu, Dayong Shen, Xiwei Liu, Fenghua Zhu, Xiuqin Shang
, Available online  
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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.
Finite-time Feedback Stabilization of a Class of Input-delay Systems with Saturating Actuators via Digital Control
Xiangze Lin, Shuaiting Huang, Wanli Zhang, Shihua Li
, Available online  , doi: 10.1109/JAS.2019.1911525 doi: 10.1109/JAS.2019.1911525
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In this paper, the problem of making an input-delay system with saturating actuators finite-time stable by virtue of digital control is investigated. A digital state feedback controller and digital observer-controller compensator are designed for two cases: when the state of the input-delay system are available or when it is unavailable. Sufficient conditions which guarantee finite-time stability of a closed-loop input-delay system are given and the proof procedure is presented in a heuristic way by constructing appropriate comparison functions. The condition can be transformed into the intersection of two curves satisfying some constraints, which reveals the relationship between designed parameters clearly. Finally, simulation results are presented to validate the method proposed in this paper.
A Novel Cascaded PID Controller for Automatic Generation Control Analysis with Renewable Sources
Behera Aurobindo, Kumar Panigrahi Tapas, K. Ray Prakash, Kumar Sahoo Arun
, Available online  
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Present day power scenarios demand a high quality uninterrupted power supply and needs environmental issues to be addressed. Both concerns can be dealt with by the introduction of the renewable sources to the existing power system. Thus, Automatic Generation Control (AGC) with diverse renewable sources and a modified-cascaded controller are presented in the paper. Also, a new hybrid scheme of the Improved Teaching Learning Based Optimization-Differential Evolution (hITLBO-DE) algorithm is applied for providing optimization of controller parameters. A study of the system with a technique such as TLBO applied to a Proportional Integral Derivative (PID), Integral Double Derivative (IDD) and PIDD is compared to hITLBO-DE tuned cascaded controller with dynamic load change.The suggested methodology has been extensively applied to a 2-area system with a diverse source power system with various operation time non-linearities such as Dead-band of, Generation Rate Constraint and reheat thermal units. The multi-area system with reheat thermal plants, hydel plants and a unit of a wind-diesel combination is tested with the cascaded controller scheme with a different controller setting for each area. The variation of the load is taken within 1% to 5% of the connected load and robustness analysis is shown by modifying essential factors simultaneously by ± 30%. Finally, the proposed scheme of controller and optimization technique is also tested with a 5-equal area thermal system with non-linearities. The simulation results demonstrate the superiority of the proposed controller and algorithm under a dynamically changing load.
Stabilization of Networked Control Systems Using a Mixed-Mode Based Switched Delay System Method
Qing-Kui Li, Xiaoli Li, Jiuhe Wang, Shengli Du
, Available online  
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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.
A User Requirement Oriented Web Service Discovery Approach Based on Logic and Threshold Petri Net
Jing Sha, Yuyue Du, Liang Qi
, Available online  
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In recent years, the number of Web services has increased significantly. Web service discovery has drawn much attention with the development of Web service applications and big data analysis. Under this circumstance, traditional Web service discovery strategies cannot adequately meet high user requirements due to the efficiency and precision of service discovery is low. In order to improve the accuracy and efficiency of service discovery, a user requirement oriented Web service discovery approach based on Petri nets is proposed in this study. A data preprocessing strategy of Web service is first designed. Then, a service clustering method is proposed based on Petri nets, which can conduct service cluster head generation, service cluster composition, and service discovery. The proposed method utilizes a superior data preprocessing method. Using simulation experiments, the efficiency and precision of Web service discovery are illustrated. Finally, the application value of the approach on real Web service is discussed.
A Novel Statistical Manifold Algorithm for Position Estimation
Bin Xia, Wenhao Yuan, Nan Xie, Caihong Li
, Available online  , doi: 10.1109/JAS.2017.7510442 doi: 10.1109/JAS.2017.7510442
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In this paper, a novel statistical manifold algorithm is proposed for position estimation of sensor nodes in a wireless network, making full use of distance information available among unknown nodes and simultaneous localization of multiple unknown nodes. To begin, a ranging model including the distance information among unknown nodes is established. With the reparameterization of the natural parameter and natural statistic, the solution problem of the ranging model is transformed into a parameter estimation problem of the curved exponential family. Then, a natural gradient method is adopted to deal with the parameter estimation problem of the curved exponential family. To ensure the convergence of the proposed algorithm, a particle swarm optimization method is utilized to obtain initial values of the unknown nodes. Experimental results indicate that the proposed algorithm can improve the positioning accuracy, compared with the traditional algorithm.
3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model
Longwei Fang, Zuowei Wang, Zhiqiang Chen, Fengzeng Jian, Shuo Li, Huiguang He
, Available online  , doi: 10.1109/JAS.2019.1911528 doi: 10.1109/JAS.2019.1911528
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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.
Convergence Analysis of a Self-Stabilizing Algorithm for Minor Component Analysis
Gang LIU, Hai-Di DONG, Ying-Bin GAO
, Available online  
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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.
Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series with Incomplete Input
Long Chen, Linqing Wang, Zhongyang Han, Jun Zhao, Wei Wang
, Available online  
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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.
Design of a Robust Optimal Decentralized PI Controller based on Nonlinear Constraint Optimization For Level Regulation: An Experimental Study
Soumya Ranjan Mahapatro, Bidyadhar Subudhi, Sandip Ghosh
, Available online  , doi: 10.1109/JAS.2019.1911516 doi: 10.1109/JAS.2019.1911516
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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.
An Improved Cooperative Team Spraying Control of a Diffusion Process with a Moving or Static Pollution Source
Juan Chen, Baotong Cui, YangQuan Chen, Bo Zhuang
, Available online  , doi: 10.1109/JAS.2019.1911519 doi: 10.1109/JAS.2019.1911519
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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.
Linguistic Single-Valued Neutrosophic Power Aggregation Operators and Their Applications to Group Decision-Making Problems
Harish Garg, Nancy
, Available online  , doi: 10.1109/JAS.2019.1911522 doi: 10.1109/JAS.2019.1911522
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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.
Flue Gas Monitoring System with Empirically-Trained Dictionary
Hui Cao, Yajie Yu, Panpan Zhang, Yanxia Wang
, Available online  
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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.
Four Wheel Independent Drive Electric Vehicle Lateral Stability Control Strategy
Yantao Tian, Xuanhao Cao, Xiaoyu Wang, Yanbo Zhao
, Available online  
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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.

IEEE/CAA Journal of Automatica Sinica

  • CiteScore 2017: 3.18
    Rank:Top 18% (Category of Control and Systems Engineering), Top 19% (Categories of Information System and Artificial Intelligence)