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39th Chinese Control Conference (CCC)

作者： Zhao, Mingming Wang, Ding Ha, Mingming Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

ISBN: (纸本)9789881563903

There always exist approximation errors during neural network control processes, which may cause the estimation value to exceed the control constraint when the optimal control input reaches to a neighborhood of the constraint. In this paper, through a new neural network training approach, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved. Based on the nonquadratic performance index and the dual heuristic dynamic programming scheme, the iterative algorithm is developed with convergence guarantee and is also implemented by using three neural networks. At last, two examples are given to demonstrate the effectiveness of the proposed optimal control scheme.

关键词： adaptive dynamic programming iterative adaptive critic control constraints neural networks nonlinear discrete-time systems

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An Event-Triggered Control Approach with Single-Critic Architecture for a Class of Nonlinear Systems

An Event-Triggered Control Approach with Single-Critic Archi...

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Chinese Automation Congress (CAC)

作者： Zhang, Kun Zhang, Huaguang Wang, Zhiliang Luo, Yanhong Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728176871

Efficient control scheme in many large-scale or long-term industrial production systems is always the core research issue for engineers and scholars. Optimal control design, which can stabilize the system dynamic, and optimize the performance index simultaneously. provides an architecture to control the systems with the minimized energy consumption. This paper proposes a novel intelligent control scheme with an event-triggered single-critic architecture, which converts the optimal nonlinear control problem into solving the well-known Hamilton-Jacobi-Bellman (HJB) equation in systems. Compared with existing methods, the event-triggered mechanism is utilized to overcome the crucial and strict requirements of the system states in real time, and the single-critic architecture simplifies the conventional structure by eliminating the actor neural network. Both the stability and convergence of the proposed algorithm have been completely proved, where the cases that event is not triggered or is triggered are considered. Finally, the effectiveness of the novel event based single-critic control approach has been demonstrated by the simulation results.

关键词： event-triggered control optimal control Hamilton-Jacobi-Bellman equation adaptive dynamic programming

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Zero-sum Neuro-optimal Control of Modular and Reconfigurable Robots with External Collisions via Critic Only Policy Iteration

Zero-sum Neuro-optimal Control of Modular and Reconfigurable...

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Chinese Automation Congress (CAC)

作者： Chen, Jingchen An, Tianjiao Wang, Yuexi Zhang, Zhenguo Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun Peoples R China

ISBN: (纸本)9781728176871

In the cause of deal with this problem of optimal position and velocity tracking regarding modular reconfigurable robots (MRRs) with external collisions, a zero-sum neural-optimal control method ground on critical only policy iteration (COPI) scheme is proposed by adaptive dynamic programming (ADP). The MRR dynamics model uncertainty is compensated by adaptive fuzzy control method. Unique the critic neural network (NN) ground on policy iteration (PI) and ADP method is used as work out hamilton-Jacobi -Issacs (HJI) equation, then the approximated optimal control is obtained. Then Lyapunov's theory proved that the MRR system was stable. At length, simulation results make clear the virtue of the proposed method.

关键词： neural network external collisions optimal control adaptive dynamic programming modular and reconfigurable robots

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Online Optimal Event-triggered Tracking Control with Actuator Saturation via ADP 9

Online Optimal Event-triggered Tracking Control with Actuato...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Liu, Lu Song, Ruizhuo Ren, Yi Zhao, Yong Song, Biao Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China China Commun Construct Intelligence Transportat C Tianjin 300210 Peoples R China Univ Sci & Technol Beijing Beijing Inst Comp Applicat Beijing 100083 Peoples R China

ISBN: (纸本)9781728159225

This paper comes up with an optimal tracking control mechanism with saturation controller for some continuous nonlinear systems based on the event-triggered (ET) mechanism via adaptive dynamic programming (ADP). To begin with, the constrained tracking control problems are able to be transformed into common event-triggered optimal control through designing an appropriate cost function. Next, a single critic network is to estimate the cost function. Moreover, taking the appropriate triggering condition as the prerequisite, the stability proof of the system is proved according to Lyapunov theory. In addition, the simulations verify the excellent performance of ET-ADP algorithm because the optimal control law is only updated at the required times influenced by the devised triggering condition, which can accomplish high efficiency of resources and energy consumption of the system.

关键词： Optimal Tracking Control Event-Triggered adaptive dynamic programming

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Optimal Fault Tolerant Control of Reconfigurable Manipulator with Actuator Saturation 32

Optimal Fault Tolerant Control of Reconfigurable Manipulator...

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32nd Chinese Control And Decision Conference (CCDC)

作者： Nie, Fujie Zhou, Fan Dong, Bo An, Tianjiao Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130000 Peoples R China

ISBN: (纸本)9781728158556

In this paper, an optimal active fault tolerant control (FTC) method is presented for reconfigurable manipulator with actuator saturation based on the adaptive dynamic programming (ADP) approach. First, the saturation constraints of the manipulator systems are tackled by using hyperbolic tangent functions. Second, an actuator fault function, which is estimated via a fault observer, is used to construct the performance index function. Then, the solution of Hamiltonian-Jacobi-Bellman (HJB) equation is solved by using ADP and policy iteration (PI) methods, and the optimal performance index function is approximated by establishing a critic neural network (NN). Based on the Lyapunov theory, the closed-loop manipulator system is proved to be asymptotic stable under the optimal FTC system. Finally, simulation results are demonstrated the validity of the proposed scheme.

关键词： adaptive dynamic programming reconfigurable manipulators actuator saturation optimal fault tolerant control neural network

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Research on Optimal Electrical Energy Management Method of Smart Grid Based on Value Iteration 3rd

Research on Optimal Electrical Energy Management Method of S...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Yan, Chunsheng Zheng, Yongjian Dai, Xun Gao, Baiyu Zhong, Liguang Xing, Shi Song, Ruizhuo Liaoning Elect Power Dev Stock Co Ltd Shenyang 110179 Peoples R China State Grid Liaoning Elect Power Supply Co Ltd Shenyang 110006 Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China

Based on the smart grid residential load data and energy storage terminal data, this paper expands a new optimal control method of smart grid supply and demand matching and optimization. This paper describes the operation principle and defines the required optimization parameters of the control object-electric energy storage management system. Aiming at the characteristics of discrete non linearity of the smart grid, a value iteration algorithm is selected to establish a control strategy,and an adaptive value iteration optimization scheme is derived. We use the BP neural network to export the control strategy of the electric energy management system by reducing the back propagation error. Finally,we use the day-ahead electricity price data to achieve numerical simulation. The result shows that the adaptive value iteration algorithm methods have higher efficiency and superior adaptability compared with the traditional BP neural network control method. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming optimal control value iteration smart grid BP neural network

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Approximate Optimal Sliding Mode Tracking Control for Modular Reconfigurable Robots Based on Critic-only Structure 3rd

Approximate Optimal Sliding Mode Tracking Control for Modula...

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3rd IFAC Workshop pn Cyber-Physical and Human Systems (CPHS)

作者： Xia, Hongbing Xia, Fuquan Zhao, Bo Huang, Yinghui Bengbu Univ Sch Elect & Elect Engn Bengbu 233030 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Bengbu Univ Sch Sci Bengbu 233030 Peoples R China

In this paper, an approximate optimal sliding mode tracking control (SMTC) strategy is investigated for modular reconfigurable robots (MRRs) through critic-only structure-based adaptive dynamic programming (ADP) scheme. The SMTC is achieved by three parts, i.e., the optimal control of the nominal system, the sliding mode-based iterative control and an adaptive robust term. The sliding mode-based iterative controller suppresses the error caused by the trajectory tracking, and the adaptive robust term is employed to ensure the reachable condition of sliding mode surface. By solving the Hamilton-Jacobi-Bellman equation with the critic neural network only, the sliding mode-based iterative control can be derived. The SMTC strategy can drive the MRR to present achieve a faster control action based on the approximate optimal control. The closed-loop MRR system is guaranteed to be asymptotically stable under the developed SMTC policy. At last, the effectiveness of the presented strategy was validated via the comparative simulation. Copyright (C) 2020 The Authors.

关键词： adaptive dynamic programming Sliding mode tracking control Neural networks Modular reconfigurable robots

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Compensator-critic structure-based neuro-optimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games

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KNOWLEDGE-BASED SYSTEMS 2021年 224卷 107100-107100页

作者： Ma, Bing Li, Yuanchun An, Tianjiao Dong, Bo Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton-Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Modular robot manipulators adaptive dynamic programming Non-zero-sum differential games Optimal control Neural networks

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Fault Tolerant Tracking Control Through Particle Swarm Optimization Based Policy Iteration 35

Fault Tolerant Tracking Control Through Particle Swarm Optim...

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35th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Liu, Xi Liu, Derong Zhao, Bo Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Beijing Normal Univ Sch Syst Sci Beijing Peoples R China

ISBN: (纸本)9781728176840

This paper focuses on fault tolerant tracking control (FTTC) problems for nonlinear systems with actuator failure. For fault-free system, the tracking control input is derived by the policy iteration. To deal with the difficulty in choosing the weight of critic neural network (CNN), the CNN is trained by the particle swarm optimization to instead the traditional gradient descent method. To handle the actuator failure, a fault observer is constructed to compensate the tracking control input, and then the fault tolerant tracking controller is derived. The developed FTTC scheme can guarantee the tracking errors to be uniformly ultimately bounded even if the system suffers form actuator faults. A simulation study is provided to illustrate the effectiveness of the designed FTTC scheme.

关键词： adaptive dynamic programming Reinforcement learning Fault tolerant control Optimal control Neural networks Particle swarm optimization

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Value Iteration Based Continuous-time Nonlinear Constrained Optimal Tracking Controller Design

Value Iteration Based Continuous-time Nonlinear Constrained ...

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Chinese Automation Congress (CAC)

作者： Xiao, Geyang Zhou, Boyang Lou, Kaiyi Chen, Zhengrong Zhejiang Lab Res Ctr Ind Internet Hangzhou Peoples R China

ISBN: (纸本)9781728176871

The value iteration mechanism is introduced to design the constrained optimal tracking controller for continuous-time nonlinear system in this paper. Unlike most studies were developed by policy iteration, the proposed VI algorithm is designed to be started with arbitrary semi-definite positive function, and thus no restriction is in the initial condition. The iterative property of the proposed algorithm is discussed and the optimality of the iterated results are obtained. To verify the effectiveness, a nonlinear case is simulated.

关键词： Constrained control adaptive dynamic programming value iteration

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