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43rd Chinese Control Conference (CCC)

作者： Huang, Tao Lu, Hongqian Yang, Yefeng Huang, Xianlin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong 100872 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

This paper introduces a novel robust cost function for the ADP algorithm to suppress the impact of the disturbance for partially unknown second-order nonlinear systems. The sliding mode control is employed to design the cost function, which thereby enhances the performance of the closed-loop system. Furthermore, an offline disturbance compensator is proposed to further improve the robustness of the system. Subsequently, the trajectory of the entire closed-loop system is proved to be uniformly ultimate bounded (UUB) in a Lyapunov sense. Sufficient simulations are conducted to verify the effectiveness and superiority of the proposed method.

关键词： Sliding mode control adaptive dynamic programming Robust cost function Disturbance compensator

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Optimal Tracking Control for a Class of Continuous Time Complex-valued Systems Based on adaptive dynamic programming Algorithm 33

Optimal Tracking Control for a Class of Continuous Time Comp...

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33rd Chinese Control Conference (CCC)

作者： Song, Ruizhuo Lewis, Frank L. Wei, Qinglai Xiao, Wendong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Univ Texas Arlington Res Inst Ft Worth TX 76013 USA Northeastern Univ State Key Lab Synthet Proc Automat Shenyang 110819 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563842

Based on the adaptive dynamic programming (ADP) algorithm, a new optimal tracking control method is proposed for a class of complex-valued nonlinear systems in this paper. The tracking error is first defined, and then the performance index function which is about the tracking error is given. By transformation, the complex-valued tracking error system is mapped into real-domain. The optimal control method for the tracking error system is presented. The two neural networks are used to be as the critic network and action network, respectively. It is proven that the closed-loop system is uniformly ultimately bounded based on Lyapunov technique. Finally, the simulation study is given to show the effectiveness of the proposed optimal tracking control method for complex-valued nonlinear system.

关键词： adaptive dynamic programming optimal control neural network complex-valued

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Discrete-Time Two-Player Zero-Sum Games for Nonlinear Systems Using Iterative adaptive dynamic programming 13th

Discrete-Time Two-Player Zero-Sum Games for Nonlinear System...

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13th International Symposium on Neural Networks (ISNN)

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9783319406633;9783319406626

This paper is concerned with a discrete-time two-player zero-sum game of nonlinear systems, which is solved by a new iterative adaptive dynamic programming (ADP) method. In the present iterative ADP algorithm, two iteration procedures, which are upper and lower iterations, are implemented to obtain the upper and lower performance index functions, respectively. Initialized by an arbitrary positive semi-definite function, it is shown that the iterative value functions converge to the optimal performance index function if the optimal performance index function of the two-player zero-sum game exists. Finally, simulation results are given to illustrate the performance of the developed method.

关键词： adaptive critic designs adaptive dynamic programming Approximate dynamic programming Neuro-dynamic programming Zero-sum game Optimal control

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Optimal Tracking Control For A Two-link Robotic Manipulator Via adaptive dynamic programming

Optimal Tracking Control For A Two-link Robotic Manipulator ...

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Chinese Automation Congress (CAC)

作者： Yang, Jiae Wang, Yujia Wang, Tong Yu, Xinghu Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin Peoples R China Harbin Inst Technol Ningbo Inst Intelligent Equipment Technol Ningbo Peoples R China

ISBN: (纸本)9781728176871

An optimal tracking control strategy for a two-link robotic manipulator by using adaptive dynamic programming is investigated in this paper. Generally, system transformation is required when applying adaptive dynamic programming (ADP) technique to solve the issue of output tracking control design, which increases the difficulty and complexity in designing optimal output tracker. In order to overcome such obstacle, a novel designed cost function is employed such that the process of control design is simplified. Meanwhile, the optimal controller concerning the controlled system is obtained. The investigated novel optimal control strategy is conducted on a two-link manipulator control system, and simulation results prove that the presented scheme is efficient and feasible.

关键词： Robot manipulator Optimal tracking control adaptive dynamic programming Cost function

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Bias-Policy Iteration Based adaptive dynamic programming for Linear Fully Actuated Systems 3

Bias-Policy Iteration Based Adaptive Dynamic Programming for...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Jiang, Huaiyuan Yang, Xuefei Xu, Chuanchuan Zhang, Kangkang Harbin Inst Technol Ctr Control Theory & Guidance Technol POB 416 Harbin 150001 Peoples R China Nanjing Res Inst Elect Engn Nanjing 210000 Peoples R China Univ Hong Kong Dept Mech Engn Hong Kong Peoples R China

ISBN: (纸本)9798350373707;9798350373691

In this paper, the bias-policy iteration based optimal control of fully actuated systems is further reconsidered. With the help of the fully actuated property, a more neat bias-PI method for the fully actuated cases is established with the convergence proof. The data-driven implementation for the proposed algorithm is introduced accordingly. A numerical example verifies the effectiveness of the proposed results.

关键词： Fully actuated systems adaptive dynamic programming Data driven control Bias-policy iteration

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Data-Driven CACC of Vehicular Platoons Based on adaptive dynamic programming with Initial Excitation 39

Data-Driven CACC of Vehicular Platoons Based on Adaptive Dyn...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Yang, Xin Liu, Zhongchang Zhao, Wenyan Dalian Maritime Univ Coll Marine Elect Engn Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper studies the cooperative adaptive cruise control (CACC) problem for a platoon of vehicles with unknown system parameters. Data-based adaptive dynamic programming (ADP) method is adopted to solve the control gains of the vehicles from algebraic Ricatti equations by using online data. The ADP takes a two-layer filter structure, which only requires the initial excitation (IE) condition instead of the restricted persistent excitation (PE) condition when solving the iterative equation. Moreover, data-storage for integral computation is obviated compared with existing data-driven approaches. The simulation results show the effectiveness of the proposed method.

关键词： adaptive dynamic programming cooperative adaptive cruise control (CACC) filter-based design

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Robust Cooperative Control for Nonlinear Multi-agent Systems with Input-Disturbances via adaptive dynamic programming 33

Robust Cooperative Control for Nonlinear Multi-agent Systems...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Qu, Qiuxia Wang, Juan QiongXia Sun, Liangliang Cui, Yang Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang 110168 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

ISBN: (纸本)9781665440899

Considering the leader-following consensus problem for the nonlinear multi-agent systems with bounded input-disturbances under fixed topology, a novel distributed robust protocol is designed to guarantee all followers synchronize to the leader by investigating the gain of the Nash Equilibrium. The robustness restrictions are given through Lyapunov theory. To get the Nash solution, critic neural networks are trained based on adaptive dynamic programming algorithm in an online and forward-in-time manner to solve the coupled Hamilton-Jacobi equations. An additional term is added to the neural network weight tuning law to avoid the requirement for the initial admissible control law.

关键词： adaptive dynamic programming Input-disturbance Leader-following consensus problem Multi-agent systems Nash equilibrium Neural network

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An Energy Management System Based on adaptive dynamic programming for Microgrid Economic Operation

An Energy Management System Based on Adaptive Dynamic Progra...

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Chinese Automation Congress (CAC)

作者： Yuan, Jun Chen, Zitao Wang, Xiaodong Zeng, Xianxian Zhang, Yun Guangdong Univ Technol Sch Automat Guangzhou Peoples R China

ISBN: (纸本)9781728176871

This paper proposes a new home energy management system (EMS) based on the adaptive dynamic programming (ADP) algorithm for economic dispatch problem of microgrid with distributed generations. The electricity price, maximum power output of renewable generation, and load are considered as uncontrollable state variables in this paper. Then two neural networks are used to implement the ADP to minimize a given utility function over a certain time horizon. In this work, the fluctuating amounts of available renewable energy are trained and evaluated in this ADP approach. EMS reduces the daily household energy costs, maximizing the PV self-consumption and increase customer benefits through economically managing the operation of the battery. Then demonstrated via computer simulation that the EMS can reduce the overall energy cost.

关键词： adaptive dynamic programming neural networks smart grid energy management system

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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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A New Discrete-Time Iterative adaptive dynamic programming Algorithm Based on Q-Learning 12th

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12th International Symposium on Neural Networks (ISNN)

ISBN: (纸本)9783319253930;9783319253923

In this paper, a novel Q-learning based policy iteration adaptive dynamic programming (ADP) algorithm is developed to solve the optimal control problems for discrete-time nonlinear systems. The idea is to use a policy iteration ADP technique to construct the iterative control law which stabilizes the system and simultaneously minimizes the iterative Q function. Convergence property is analyzed to show that the iterative Q function is monotonically non-increasing and converges to the solution of the optimality equation. Finally, simulation results are presented to show the performance of the developed algorithm.

关键词： adaptive critic designs adaptive dynamic programming approximate dynamic programming Q-learning policy iteration neural networks nonlinear systems optimal control

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