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adaptive dynamic programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks

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SENSORS 2018年第12期18卷 4090-4090页

作者： Liu, Fen Xiao, Wendong Chen, Shuai Jiang, Chengpeng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Beijing Engn Res Ctr Ind Spectrum Imaging Beijing 100083 Peoples R China

Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose new technical challenges for collaborative target tracking on how to schedule sensors over the infinite horizon under the restriction on limited sensor energy harvesting capabilities. In this paper, we propose a novel adaptive dynamic programming (ADP)-based multi-sensor scheduling algorithm (ADP-MSS) for collaborative target tracking for energy-harvesting WSNs. ADP-MSS can schedule multiple sensors for each time step over an infinite horizon to achieve high tracking accuracy, based on the extended Kalman filter (EKF) for target state prediction and estimation. Theoretical analysis shows the optimality of ADP-MSS, and simulation results demonstrate its superior tracking accuracy compared with an ADP-based single-sensor scheduling scheme and a simulated-annealing based multi-sensor scheduling scheme.

关键词： energy harvesting wireless sensor networks adaptive dynamic programming target tracking extended Kalman filter sensor scheduling

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Event-Triggered adaptive dynamic programming for Continuous-Time Nonlinear Two-Player Zero-Sum Game 25th

Event-Triggered Adaptive Dynamic Programming for Continuous-...

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25th International Conference on Neural Information Processing (ICONIP)

作者： Xue, Shan Luo, Biao Liu, Derong Li, Yueheng Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

ISBN: (纸本)9783030042394;9783030042387

In this paper, an event-triggered adaptive dynamic programming (ADP) algorithm is developed to solve the two-player zero-sum game problem of continuous-time nonlinear systems. First, a critic neural network is employed to approximate the optimal value function. Then, an event-triggered ADP method is proposed, which guarantees the stability of the closed-loop system. The developed method can save the amount of computation as the control law and disturbance law that update only when the pre-designed triggering condition is violated. Finally, its effectiveness is verified through simulation results.

关键词： Event-triggering control adaptive dynamic programming Two-player zero-sum game Hamilton-Jacobi-Isaacs equation

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A Low-Power Circuit for adaptive dynamic programming 31

A Low-Power Circuit for Adaptive Dynamic Programming

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31st International Conference on VLSI Design / 17th International Conference on Embedded Systems (VLSID & ES))

作者： Zheng, Nan Mazumder, Pinaki Univ Michigan Elect Engn & Comp Sci Dept Ann Arbor MI 48109 USA

ISBN: (纸本)9781538636923

This paper presents a low-power CMOS design for accelerating an adaptive dynamic programming algorithm, called action-dependent heuristic dynamic programming, which is widely employed in many real-life control problems. The objective of this work is to solve Bellman equation approximately using efficient hardware in order to generate near-optimal real-time control policies for many control applications. The hardware exploits the data-level parallelism exists in both inference and learning of neural networks in order to improve throughput as well as to provide good scalability. The circuit is realized in a 65-nm technology. It is shown with simulations that the design is two orders of magnitude faster than a software running on a general-purpose processor thanks to the parallelization of the algorithm and the reduction in unnecessary control overheads. Performance of the CMOS circuit is benchmarked with two popular control tasks. Successful learning can be achieved with a power consumption of 28 mW.

关键词： Reinforcement learning adaptive dynamic programming neural networks low-power CMOS circuit

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Data-Driven adaptive dynamic programming for H₂/H_∞ Control of Unknown Nonlinear System 37

Data-Driven Adaptive Dynamic Programming for H<sub>2</sub>/H...

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37th Chinese Control Conference (CCC)

作者： Pu, Jun Ma, Qingliang Gu, Fan Li, Yuandong Xian Res Inst High Tech Xian 710025 Shaanxi Peoples R China

ISBN: (纸本)9789881563958

In order to solve the practical problems that exact analytic solution of the coupled Hamilton-Jacobi-Isaacs ( HJI) equations arising from the mixed H-2 / H-infinity control of nonlinear systems and the nonlinear system dynamics models is not generally known. According to the model-based iterative algorithm, a data-driven approximate dynamic programming algorithm for solving mixed H-2 / H-infinity control problems is derived by adding known noise into control strategy and disturbance strategy. The Nash equilibrium strategy of nonlinear system is obtained online by using input-output data of nonlinear system, which does not depend on the specific model information of the system. Two critic neural networks and two action neural networks are used to synchronously update two value functions, control strategy and disturbance strategy online. The unknown parameters of neural network are estimated by generalized least squares. The simulation results verify the feasibility of the algorithm.

关键词： adaptive dynamic programming data-driven H-2 / H-infinity control Optimum control Neural network

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H_∞ Control of Constrained-Input Nonlinear Systems with Unknown Model Based on adaptive dynamic programming 30

H<sub>∞</sub> Control of Constrained-Input Nonlinear System...

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30th Chinese Control and Decision Conference (CCDC)

作者： Pu, Jun Ma, Qingliang Gu, Fan Yu, Zexiang Xian Res Inst High Tech Dept Control Engn Xian 710025 Peoples R China

ISBN: (纸本)9781538612446

An adaptive dynamic programming(ADP) algorithm that contain online measurement and off-policy learning two phase is proposed to solve the H-infinity control problem of continuous-time nonlinear system with constrained -input and unknown model only based on online data. The model -free Hamiton-Jacobi-Isaacs(HJI) equation is derived by the policy iteration(PI) and the model-free iteration reinforcement learning(IRL) method. Three neural networks(NN) are structured, after collecting online data of system is finished, then off-policy learning method is used to approximate solve the model-free HJI equation. And the value function, control strategy and disturbance strategy are obtained by the NN. The weights of the neural network are solved by the least square method. The simulation results verify the feasibility of the algorithm.

关键词： adaptive dynamic programming H-infinity Control Constrained-Input Neural Network

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Optimal tracking control for linear discrete-time stochastic system based on adaptive dynamic programming

Optimal tracking control for linear discrete-time stochastic...

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第三十八届中国控制会议

作者： Fang Wang Xin Chen Wei Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

In this paper, an adaptive dynamic programming(ADP) algorithm based on value iteration is proposed to tackle the stochastic linear quadratic(SLQ) optimal tracking control problem for discrete-time systems subject to multiplicative ***, an augmented system made up of the original system and the command generator is constructed and the condition of well-posedness for SLQ problem is given. Next, the SLQ problem is converted into the deterministic problem through system transformation. Then, ADP algorithm is utilized to solve the SLQ problem with convergence analysis. In the iteration process of ADP algorithm, the system dynamics are not essential to solve the Bellman equation but are needed to update the control gain matrix. Finally, simulation results have shown that the proposed scheme for the problem gives good tracking performance.

关键词： Stochastic system optimal tracking control adaptive dynamic programming

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adaptive dynamic programming for Optimal Control of van der Pol Oscillator

Adaptive Dynamic Programming for Optimal Control of van der ...

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第30届中国控制与决策会议

作者： Zheng Ji Xuyang Lou School of Internet of Things Engineering Jiangnan University

adaptive dynamic programming has been considered as one of the effective methods to solve the optimal control of nonlinear systems and it has lots of applications in academic research and engineering. Firstly, we formulate the optimal control of van der Pol oscillator with the least energy consumption. Then, using an adaptive dynamic programming method to dynamically solve the approximate solution of this optimal control problem. A simplified neural network model is used to approximate the cost function. In addition, an admissible initial control policy for van der Pol oscillator is designed and Lyapunov stability criterion is established. On the basis of predecessors, we present a moving-data window recursive least-square method, which can overcome the computational difficulties and the problem that the parameter of oscillator maybe unknown and various slowly. Finally, the implementation steps of the method are proposed, and the feasibility is illustrated.

关键词： adaptive dynamic programming van der Pol oscillator neural network moving-data window recursive least-square method

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Seasonal Scheduling of Office Electricity Use via adaptive dynamic programming 37

Seasonal Scheduling of Office Electricity Use via Adaptive D...

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37th Chinese Control Conference (CCC)

作者： Shi, Guang Zhao, Bo Li, Chao Jin, Xin Liu, Derong Coordinat Ctr China Natl Comp Network Emergency Response Tech Team Beijing 100029 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789881563958

In this paper, an adaptive dynamic progranuning (ADP) based optimization method is proposed to schedule the electricity use of an office, where a battery is considered as the control variable, while solar and wind energies are included as additional energy supplies besides the grid. The electricity demand of an office generally contains socket, lighting and air-conditioning demands. Based on the periodic models of electricity price, electricity demand, and solar and wind energies, the optimal control strategies of the battery are determined by the proposed ADP based optimization method, so that the electricity cost from the grid can he saved. Simulation analysis demonstrates that the proposed method can achieve optimal real-time scheduling of office electricity use in different seasons of a year.

关键词： Smart grid electricity scheduling optimal control adaptive dynamic programming

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Optimal Sliding Mode Tracking Control for Reconfigurable Manipulators Based on adaptive dynamic programming

Optimal Sliding Mode Tracking Control for Reconfigurable Man...

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Chinese Automation Congress (CAC)

作者： Ma, Bing Dong, Bo Zhou, Fan Li, Ruiyang Yu, Miao Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Jilin Peoples R China CRRC Changchun Railway Vehicles CO LTD Changchun Jilin Peoples R China

ISBN: (纸本)9781728113128

This paper presents a novel optimal sliding mode tracking control method for reconfigurable manipulators based on the policy iteration (PI) and adaptive dynamic programming (ADP). The designed sliding mode tracking control can suppress the tracking error caused by reconfiguration of the manipulators. Based on ADP and PI algorithm, the Hamiltonian-Jacobi-Bellman (HJB) equation can be solved by constructing a critic neural network and then the approximated optimal sliding mode control policy can be derived directly. Based on the Lyapunov stability theorem, the closed-loop robotic system is proved to be asymptotic stability. Finally, simulations are provided to demonstrate the effectiveness of the proposed method.

关键词： reconfigurable manipulators adaptive dynamic programming critic neural network sliding mode control optimal control

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Model Free Optimal Integral Sliding Mode Control for Reconfigurable Manipulators Based on adaptive dynamic programming

Model Free Optimal Integral Sliding Mode Control for Reconfi...

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Chinese Automation Congress (CAC)

作者： An, Tianjiao Qin, Yi Wang, Shuxiang Zhou, Fan Liu, Keping Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Jilin Peoples R China

ISBN: (纸本)9781728113128

In this paper, a model-free integral sliding mode control method is presented via adaptive dynamic programming (ADP) to address the trajectory tracking problem of reconfigurable manipulators. The integral sliding mode control method is adopted to deal with the model uncertainties of joint subsystems and a local neural network identifier is designed to compensate the effect of the interconnection dynamic couplings (IDCs) between the subsystems. Based on the ADP and the policy iteration (PI) algorithm, the Hamilton-Jacobi-Bellman (HJB) equation can be solved by using critic neural network and then we can get the optimal control strategy. Based on the Lyapunov theory, the closed-loop manipulator system is proved to be asymptotic stability. Finally, simulation results are demonstrated the effectiveness of the proposed method.

关键词： reconfigurable manipulators integral sliding mode model-free control adaptive dynamic programming

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