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31st Chinese Control And Decision Conference (CCDC)

作者： Jiang, Chengpeng Liu, Fen Chen, Shuai Xiao, Wendong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728101057

Collaborative target tracking is a typical application of wireless sensor networks (WSNs), in which sensors shall be scheduled for trading off between the tracking performance and the energy utilization. Energy harvesting in wireless sensor networks is attractive for the continuous operation of the network, but poses new challenges for collaborative target tracking due to the limited energy harvesting capabilities of the nodes. In this paper, we will propose a novel finite-horizon adaptive dynamic programming (FHADP) based sensor scheduling for collaborative target tracking in an energy harvesting WSN. Unscented Kalman filter (UKF) is used for prediction and estimation of the tracking performance, and multiple-step sensor scheduling is performed using ADP based on the predictive harvested energy of the nodes and tracking performance. Simulation results show that FHADP based sensor scheduling scheme can obtain superior tracking performance compared with ADP.

关键词： wireless sensor network target tracking energy harvesting adaptive dynamic programming unscented Kalman filter

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Optimal tracking control for linear discrete-time stochastic system based on adaptive dynamic programming 38

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

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38th Chinese Control Conference (CCC)

作者： Wang, Fang Chen, Xin Wang, Wei China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan Peoples R China

ISBN: (纸本)9789881563972

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 noise. Firstly. 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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Fault Tolerant Control for Nonlinear Systems Based on adaptive dynamic programming with Particle Swarm Optimization 10

Fault Tolerant Control for Nonlinear Systems Based on Adapti...

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10th International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Lin, Haowei Wu, Qiuye Liu, Derong Zhao, Bo Yang, Qinmin Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou Peoples R China

ISBN: (纸本)9781728100159

This paper develops a fault tolerant control (FTC) scheme based on adaptive dynamic programming(ADP) employing the particle swarm optimization (PSO) for nonlinear systems with actuator failures. Using the well-known ADP method, the solution of Hamilton-Jacobi-Bellman equation (HJBE) is approximated by constructing a critic neural network (CNN) which is trained by the PSO algorithm. Compared to the existing gradient descent-trained CNN, the PSO-trained CNN has a higher success rate in solving the HJBE. In order to eliminate the impact of the actuator failure, the ADP-based FTC strategy is developed to guarantee the closed-loop system to be ultimately uniformly bounded (UUB). Finally, a simulation example is provided to demonstrate the effectiveness of the developed method.

关键词： adaptive dynamic programming Fault tolerant control Particle swarm optimization Neural network

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adaptive dynamic programming for Optimal Control of van der Pol Oscillator 30

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

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30th Chinese Control and Decision Conference (CCDC)

作者： Ji, Zheng Lou, Xuyang Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781538612446

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 arc 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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Active Optimal Fault-Tolerant Control Method for Multi-fault Concurrent Modular Manipulator Based on adaptive dynamic programming 16th

Active Optimal Fault-Tolerant Control Method for Multi-fault...

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16th International Symposium on Neural Networks (ISNN)

作者： Li, Bing Zhou, Fan Dong, Bo Liu, Yucheng Liu, Fu Lu, Huiqiu Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China Jilin Univ Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9783030228088;9783030228071

In this paper, a novel active optimal fault-tolerant control (FTC) scheme is designed based on adaptive dynamic programming (ADP) for modular manipulator when sensor and actuator faults are concurrency. Firstly, the sensor fault is transformed into the pseudo-actuator fault by constructing a nonlinear transformation with diffeomorphism theory. Secondly, the faults estimated by the adaptive fault observer are applied to establish an improved performance index function. Next, the online policy iteration (PI) algorithm is used to solve the Hamilton-Jacobi-Bellman (HJB) equation via establishing a critic neural network. The optimal fault-tolerant controller is proved to be uniformly ultimately bounded (UUB) based on Lyapunov stable theory. Finally, the effectiveness of the proposed multi-fault-tolerant control algorithm is verified by simulation results.

关键词： adaptive dynamic programming Modular manipulators Optimal fault-tolerant control Critic neural network Policy iteration

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Data-based Optimal Control for Discrete-time Systems via Deep Deterministic Policy Gradient adaptive dynamic programming 9

Data-based Optimal Control for Discrete-time Systems via Dee...

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9th International Conference on Information Science and Technology (ICIST)

作者： Zhang, Yongwei Lin, Mingduo Liu, Derong Qin, Yuwen Zhao, Bo Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China

ISBN: (纸本)9781728121062

The model-free optimal control problem for discrete-time systems is considered in this paper by using deep deterministic policy gradient adaptive dynamic programming (DDPGADP) algorithm. The system data is obtained by using the off-policy learning and the control law is updated by policy gradient. The convergence of DDPGADP algorithm is verified by showing that the Q-function sequence is monotonically non-increasing and converges to the optimum. In order to implement this method, an actor-critic neural network structure is established by adopting the target network technology from deep Q-learning during the neural network training process. Finally, simulation examples are presented to verify the effectiveness of the proposed method.

关键词： adaptive dynamic programming Deep deterministic policy gradient Optimal control Neural networks Model-free

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Attitude Tracking Control for a Quadrotor via backstepping and adaptive dynamic programming 31

Attitude Tracking Control for a Quadrotor via backstepping a...

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31st Chinese Control And Decision Conference (CCDC)

作者： Chi, Wenhao Ji, Yuehui Gao, Qiang Tianjin Univ Technol Sch Elect & Elect Engn Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China

ISBN: (纸本)9781728101057

An online-learning control algorithm based on Backstepping control and action-dependent heuristic dynamic programming (ADHDP) is proposed for quadrotors to facilitate a satisfied control performance. In the paper, backstepping controller is used as the main controller and ADHDP controller is worked as the complementary controller. The ADHDP control strategy is designed based on the difference between the real attitude value response from the basic Backstepping control and the desired attitude reference value. An actor-critic neural network is served as the primary structure in ADHDP controller, which has the ability of online adaptive learning. The simulation results in Matlab/Simulink are presented to verify the effectiveness and robustness of the proposed control technology.

关键词： Attitude tracking control Quadrotor Backstopping adaptive dynamic programming

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Iterative Unit-based adaptive dynamic programming with Application to Fluid Catalytic Cracker Unit

Iterative Unit-based Adaptive Dynamic Programming with Appli...

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Chinese Automation Congress (CAC)

作者： Fu, Kaixin Zou, Yuanyuan Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Key Lab Syst Control & Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781728140940

Model predictive control(MPC) poses an significant role in process industry. However, due to the characteristics of difficult to solve and poor control performance of nonlinear MPC with disturbances, this paper proposes an iterative unit-based adaptive dynamic programming (IU-ADP) aiming to solve the nonlinear MPC problem with disturbances. in the IU-ADP structure, an actor-critic scheme is adopted together with the iterative method, in which the critic network intends to approximate the performance index, the actor network with an iterative method aims to approximate the optimal control. Moreover, IU-ADP with data parallelization can achieve fast time efficiency. Finally, it is tested on the fluid catalytic cracker unit (FCCU) plant compared with the traditional MPC method. The results illustrate that IU-ADP obtains a little better control performance.

关键词： adaptive dynamic programming iterative method nonlinear predictive control

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An Optimization Method Based on adaptive dynamic programming for Cleaning Photovoltaic Panels

An Optimization Method Based on Adaptive Dynamic Programming...

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第三十九届中国控制会议

作者： Siyuan Fan Xianshuang Yao Shengxian Cao Bo Zhao School of Automation Engineering Northeast Electric Power University

Photovoltaic（PV） power generation has become one of the major energy in all the world. If the dust accumulation of PV power station is not cleaned in time, the production cost will be increased in power station. This paper seeks to develop a optimization model of cleaning to inform economic operation decision for PV power station. By setting the goal of the maximum output power and the minimum economic loss, a optimization method based on adaptive dynamic programming（ADP） is proposed. A new cleaning strategy is proposed in this paper, and the simulation results show the effectiveness of this method. Taking a 12 WM PV power station in western Jilin Province in China as an example, the total maintenance cost of cleaning every 17 days is the lowest.

关键词： adaptive dynamic programming optimization method PV panels cleaning strategy

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Online adaptive dynamic programming based on echo state networks for dissolved oxygen control

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APPLIED SOFT COMPUTING 2018年 62卷 830-839页

作者： Bo Ying-Chun Zhang Xin China Univ Petr Coll Informat & Control Engn Qingdao 266580 Peoples R China

To solve the control problem of dissolved oxygen concentration in wastewater treatment process (WWTP), an online adaptive dynamic programming (ADP) scheme based on echo state networks (ESN) is proposed. The ESN-based ADP controller contains three modules composed of echo state networks, and the three modules approximate respectively the critic index, the system model and the optimal control policy. Further, an online learning algorithm on the basis of recursive least squares (RLS) is designed to train the ESN-based ADP controller, and also the convergence of the online learning algorithm is investigated. The design and operation of the ESN-based ADP controller depend mainly on the online data observed from controlled system, and minor prior knowledge is required. The proposed ESN-based ADP controller's performance is tested and evaluated on Benchmark Simulation Model no. 1 (BSM1)-a general WWTP benchmark. Experimental results demonstrate that the proposed controller can achieve high performance on dissolved oxygen level control in WWTP. (C) 2017 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Echo state network Online learning Dissolved oxygen Wastewater treatment

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