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检索条件"主题词=Adaptive Dynamic Programming"

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Optimal Event-Triggered H∞ Control for Nonlinear Systems with Completely Unknown dynamics 41

Optimal Event-Triggered H∞ Control for Nonlinear Systems wi...

引用

41st Chinese Control Conference (CCC)

作者： Chu, Kun Peng, Zhinan Zhang, Zhiquan Huang, Rui Shi, Kecheng Cheng, Hong Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9789887581536

In this paper, a novel identifier-critic-based event-triggered control method is proposed to solve the H8 control problem of unknown nonlinear systems. First, system identification is proposed to reconstruct the completely unknown system dynamics, and neural networks (NNs) based critic learning framework is established to online approximated cost function and obtain corresponding optimal control law. It is highlighted that new NNs weights adaption rules based on a parameter estimation method are designed for the identifier-critic framework. In the meantime, a static event-triggered mechanism is integrated into the proposed identifier-critic architecture to improve communication and computation efficiency. The stability of the control system and the convergence of NN weights are both theoretically proved under the proposed control method. Simulation studies are provided to demonstrate the effectiveness of the proposed H-infinity control method.

关键词： Optimal control nonlinear systems adaptive dynamic programming event-triggered mechanism

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Event-Triggered adaptive Control for Discrete-Time Zero-Sum Games

Event-Triggered Adaptive Control for Discrete-Time Zero-Sum ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Ziyang Wei, Qinglai Liu, Derong Luo, Yanhong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Guangdong Univ Technol Sch Automat Guangzhou Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781728119854

In this paper, an event-triggered adaptive dynamic programming (ADP) method is developed for the discrete-time nonlinear two-player zero-sum games. First, an event-triggered ADP algorithm is presented to solve the Hamilton-Jacobi-Isaacs (HJI) equation. Then, a novel double event-triggered scheme is designed, the control inputs and the disturbance inputs will be updated only when the triggering conditions are satisfied. Therefore, the computational burden and the communication cost can be reduced. The algorithm is implemented by two neural networks, and the stability of the two-player system is proved. Finally, an example is employed to illustrate the effectiveness of the developed method.

关键词： adaptive dynamic programming neural network event-triggered control zero-sum game

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adaptive Optimal Control for Large-Scale Systems based on Robust Policy Iteration 34

Adaptive Optimal Control for Large-Scale Systems based on Ro...

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34th Chinese Control and Decision Conference (CCDC)

作者： Zhao, Fuyu Zhao, Liang Shandong Univ Sci Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Qingdao Univ Inst Complex Sci Shandong Key Lab Ind Control Technol Qingdao 266071 Peoples R China

ISBN: (纸本)9781665478960

In this paper, a robust policy iteration (PI) based adaptive optimal control approach is proposed for large-scale systems. The parametric and dynamic uncertainties are coped with by using the robust adaptive dynamic programming and small-gain theory. Based on the robust policy iteration, the learned control policy is composed of the measurement state and input. The closed-loop stability and the convergence of the proposed algorithms are analyzed by using Lyapunov stability theory and small-gain techniques. A practical example of multimachine power systems with governor controllers is given to demonstrate the effectiveness of the proposed method.

关键词： Optimal control adaptive dynamic programming robust policy iteration large-scale systems

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adaptive Droop-Based Active Power Curtailment Method for Overvoltage Prevention in Low Voltage Distribution Network

Adaptive Droop-Based Active Power Curtailment Method for Ove...

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IEEE International Conference on Electro Information Technology (EIT)

作者： Maharjan, Manisha Tamrakar, Ujjwol Malla, Naresh dos Reis, Fernando B. Ni, Zhen Hansen, Timothy M. Tonkoski, Reinaldo South Dakota State Univ Dept Elect Engn & Comp Sci Brookings SD 57007 USA

ISBN: (纸本)9781509047673

Clean energy incentives and the continuous fall in the cost of photovoltaic (PV) installations have led to a steady growth in residential PV systems. One of the main consequences of this high PV penetration in low voltage (LV) distribution networks is the overvoltage issue. Active power curtailment of PV inverters has been previously used to curtail the output power of the inverters below its operating point to prevent such overvoltages. This technique, however, uses a constant droop-based approach to curtail the power, based on the difference between the measured voltage and a critical voltage level. In this paper, this technique is implemented in a typical LV distribution network in North America with high PV penetration level. The simulation results show that the system undergoes excessive curtailment resulting in unnecessary energy loss. An adaptive droop-based approach using adaptive dynamic programming (ADP) is proposed as a possible solution to minimize the total energy loss in the system while keeping the system voltage under the critical operating limits. The energy loss due to curtailment has decreased by 25% after implementing the adaptive-droop based approach using ADP.

关键词： Low voltage distribution network active power curtailment overvoltage prevention adaptive dynamic programming

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adaptive-Learning-Based H_∞ Optimal Tracking Control Design for a Class of Constrained Nonlinear Systems 37

Adaptive-Learning-Based <i>H</i><sub>∞</sub> Optimal Tracki...

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37th Chinese Control Conference (CCC)

作者： Hou, Jiaxu Wang, Ding Liu, Derong 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 Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9789881563958

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H-infinity tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： adaptive dynamic programming H-infinity tracking control constraints convergence proof

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Optimal Tracking Control for Reconfigurable Manipulators Based on Critic-only Policy Iteration Algorithm 36

Optimal Tracking Control for Reconfigurable Manipulators Bas...

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第36届中国控制会议

作者： Hongbing Xia Bo Zhao Yuanchun Li Department of Control Science and Engineering Changchun University of Technology The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper tackles the optimal tracking control problem for reconfigurable manipulators based on critic-only policy iteration（Co PI） algorithm. By system transformation, the optimal tracking control problem is transformed into an optimal regulation problem. The optimal tracking controller is composed of the desired controller and the approximate optimal feedback one. The desired controller is developed to maintain the desired tracking performance at the steady-state, while the approximate optimal feedback controller is designed to stabilize the tracking error dynamics in an optimal manner. Then, a critic neural network is used to estimate the optimal performance index function, and the optimal feedback control is obtained by the Co PI algorithm. The convergence of the proposed method is analyzed and it is shown that the closed-loop system based on Co PI is uniformly ultimately bounded by using the Lyapunov approach. Finally, simulation studies are given to show the effectiveness of the developed method.

关键词： adaptive dynamic programming Reconfigurable manipulators Optimal tracking control Policy iteration Neural networks

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Optimal trajectory tracking control based on reinforcement learning for the deployment process of space tether system 6

Optimal trajectory tracking control based on reinforcement l...

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6th Conference on Advances in Control and Optimization of dynamical Systems (ACODS)

作者： Feng, Yiting Wang, Changqing Li, Aijun Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Space tether system has a wide application prospect in space mission. Due to the characteristics of strong non-linearity and under-actuation, as well as the interference of complex space environment, it is difficult to model the tethered system accurately. Hence, the controller based on the parameters of the system model will cause large errors in the process of control. In this paper, an adaptive dynamic programming algorithm based on reinforcement learning theory is adopted. By training two Back Propagation (BP) neural networks, namely critic neural network (NN) and actor NN, the perfounance index function and control law of the system approach approximate optimal values respectively. The controller design is independent of the system model, so model-free control of the system is realized by implementing this control method. First, assuming that the out-of-plane motion of the system is stable, the optimal deployment trajectory of the tethered system is obtained by parameter optimization based on Nelder-Mead method. The optimal trajectory is taken as the nominal trajectory and the trajectory tracking is carried out by reinforcement learning controller. The simulation results show that the reinforcement learning algorithm has a good control effect on the in-plane trajectory tracking of the tethered system, which proves the feasibility and robustness of the control method. (C) 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： space tether system deployment trajectory tracking neural network adaptive dynamic programming reinforcement learning

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Improved adaptive Critic for Neural Optimal Control of Constrained Nonlinear Discrete-Time Systems 39

Improved Adaptive Critic for Neural Optimal Control of Const...

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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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Incremental Model-Based Global Dual Heuristic programming for Flight Control 13th

Incremental Model-Based Global Dual Heuristic Programming fo...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on adaptive and Learning Control Systems (ALCOS)

作者： Sun, Bo van Kampen, Erik-Jan Delft Univ Technol Dept Control & Operat NL-2629 HS Delft Netherlands

This paper proposes a novel adaptive dynamic programming method, called Incremental model-based Global Dual Heuristic programming, to generate a self-learning adaptive controller, in the absence of sufficient prior knowledge of system dynamics. An incremental technique is employed for online model identification, instead of the artificial neural networks commonly used in conventional Global Dual Heuristic programming. The incremental model has the capability of tackling nonlinearity and uncertainty of the plant, but can also guarantee high precision of online identification without the requirement of offline training. On the basis of the identified model, two neural networks are adopted to facilitate the implementation of the self-learning controller, by approximating the cost-to-go and its derivatives and the control policy, respectively. Both methods are applied to a tracking control problem of a nonlinear aerospace system and the results show that the proposed method outperforms conventional Global Dual Heuristic programming in online learning speed, tracking precision and robustness to variation of initial system states and network weights. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： adaptive dynamic programming adaptive control incremental technique global dual heuristic programming artificial neural network

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Discrete-Time Generalized Policy Iteration ADP Algorithm With Approximation Errors

Discrete-Time Generalized Policy Iteration ADP Algorithm Wit...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Wei, Qinglai Li, Benkai Song, Ruizhuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781538627266

This paper concerns with a novel generalized policy iteration (GPI) algorithm with approximation errors. Approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The convergence of the developed algorithm is established to show that the iterative value function is convergent to a finite neighborhood of the optimal performance index function. Finally, numerical examples and comparisons are presented.

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

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