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Guaranteed cost neural tracking control for a class of uncertain nonlinear systems using adaptive dynamic programming

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NEUROCOMPUTING 2016年 198卷 80-90页

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

This paper presents an adaptive dynamic programming-based guaranteed cost neural tracking control algorithm for a class of continuous-time matched uncertain nonlinear systems. By introducing an augmented system and employing a modified cost function with a discount factor, the guaranteed cost tracking control problem is transformed into an optimal tracking control problem. Unlike existing optimal tracking control algorithms often requiring the control matrix to be invertible, the developed control algorithm relaxes this restrictive condition under the assumption that the system is controllable. A single critic neural network (NN) is constructed to approximate the solution of the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal augmented error dynamics. Utilizing the newly developed critic NN, the optimal tracking control can be derived without policy iteration. All signals in the closed-loop system are proved to be uniformly ultimately bounded via Lyapunov's direct method. In addition, the developed control scheme is verified to guarantee that the tracking errors converge to an adjustable neighborhood of the origin. Two numerical examples are provided to illustrate the effectiveness and applicability of the developed approach. (C) 2016 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Guaranteed cost control Hamilton-Jacobi-Bellman equation Neural network Nonlinear system Reinforcement learning

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Nearly finite-horizon optimal control for a class of nonaffine time-delay nonlinear systems based on adaptive dynamic programming

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NEUROCOMPUTING 2015年 156卷 166-175页

作者： Song, Ruizhuo Wei, Qinglai Sun, Qiuye 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 Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Liaoning Peoples R China

In this paper, a novel adaptive dynamic programming (ADP) algorithm is developed to solve the nearly optimal finite-horizon control problem for a class of deterministic nonaffine nonlinear time-delay systems. The idea is to use ADP technique to obtain the nearly optimal control which makes the optimal performance index function close to the greatest lower bound of all performance index functions within finite time. The proposed algorithm contains two cases with respective different initial iterations. In the first case, there exists control policy which makes arbitrary state of the system reach to zero in one time step. In the second case, there exists a control sequence which makes the system reach to zero in multiple time steps. The state updating is used to determine the optimal state. Convergence analysis of the performance index function is given. Furthermore, the relationship between the iteration steps and the length of the control sequence is presented. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of ADP iteration algorithm. At last, two examples are used to demonstrate the effectiveness of the proposed ADP iteration algorithm. (C) 2014 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Approximate dynamic programming adaptive critic designs Nonlinear systems Optimal control Time-delay

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Finite-horizon differential games for missile-target interception system using adaptive dynamic programming with input constraints

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2018年第2期49卷 264-283页

作者： Sun, Jingliang Liu, Chunsheng Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing Jiangsu Peoples R China

In this paper, the problem of intercepting amanoeuvring target within a fixed final time is posed in a non-linear constrained zero-sum-differential game framework. The Nash equilibrium solution is found by solving the finite-horizon constrained differential game problem via adaptive dynamic programming technique. Besides, a suitable non-quadratic functional is utilised to encode the control constraints into a differential game problem. The single critic network with constant weights and time varying activation functions is constructed to approximate the solution of associated time-varying Hamilton-Jacobi-Isaacs equation online. To properly satisfy the terminal constraint, an additional error term is incorporated in a novel weight-updating law such that the terminal constraint error is also minimised over time. By utilising Lyapunov's direct method, the closed-loop differential game system and the estimation weight error of the critic network are proved to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is demonstrated by using a simple non-linear system and a non-linear missile-target interception system, assuming first-order dynamics for the interceptor and target.

关键词： Differential game finite-horizon adaptive dynamic programming input constraints missile-target interception

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Online optimal tracking control of continuous-time linear systems with unknown dynamics by using adaptive dynamic programming

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INTERNATIONAL JOURNAL OF CONTROL 2014年第5期87卷 1000-1009页

作者： Qin, Chunbin Zhang, Huaguang Luo, Yanhong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Henan Univ Basic Expt Teaching Ctr Kaifeng 475004 Henan Peoples R China

In this paper, a novel theoretic formulation based on adaptive dynamic programming (ADP) is developed to solve online the optimal tracking problem of the continuous-time linear system with unknown dynamics. First, the original system dynamics and the reference trajectory dynamics are transformed into an augmented system. Then, under the same performance index with the original system dynamics, an augmented algebraic Riccati equation is derived. Furthermore, the solutions for the optimal control problem of the augmented system are proven to be equal to the standard solutions for the optimal tracking problem of the original system dynamics. Moreover, a new online algorithm based on the ADP technique is presented to solve the optimal tracking problem of the linear system with unknown system dynamics. Finally, simulation results are given to verify the effectiveness of the theoretic results.

关键词： continuous-time linear system optimal tracking problem adaptive dynamic programming

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Model-free optimal control design for a class of linear discrete-time systems with multiple delays using adaptive dynamic programming

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NEUROCOMPUTING 2014年 135卷 163-170页

作者： Zhang, Jilie Zhang, Huaguang Luo, Yanhong Feng, Tao Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

In this paper, a model-free optimal control scheme for a class of linear discrete-time systems with multiple delays in state, control and output vectors is proposed. The optimal control can be obtained using only measured input/output data from systems, by adaptive dynamic programming (ADP) technology. First, we give a class of systems what we want to address. Then, a model-free optimal control is designed to minimize the given cost functional by ADP technology, which combines a similar Q-learning method with a value iteration (VI) algorithm, using only the measured input/output data. Finally, several numerical examples are given to illustrate the effectiveness of our approach. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Model-free optimal control Discrete-time delay system Optimal control adaptive dynamic programming

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A three-network architecture for on-line learning and optimization based on adaptive dynamic programming

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NEUROCOMPUTING 2012年第1期78卷 3-13页

作者： He, Haibo Ni, Zhen Fu, Jian Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China

In this paper, we propose a novel adaptive dynamic programming (ADP) architecture with three networks, an action network, a critic network, and a reference network, to develop internal goal-representation for online learning and optimization. Unlike the traditional ADP design normally with an action network and a critic network, our approach integrates the third network, a reference network, into the actor-critic design framework to automatically and adaptively build an internal reinforcement signal to facilitate learning and optimization overtime to accomplish goals. We present the detailed design architecture and its associated learning algorithm to explain how effective learning and optimization can be achieved in this new ADP architecture. Furthermore, we test the performance of our architecture both on the cart-pole balancing task and the triple-link inverted pendulum balancing task, which are the popular benchmarks in the community to demonstrate its learning and control performance over time. (C) 2011 Elsevier B.V. All rights reserved.

关键词： adaptive dynamic programming Online learning and control Actor-critic design Three-network architecture Multi-state optimization Goal representation Reinforcement learning

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Data-Driven adaptive dynamic programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown dynamics

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IEEE ACCESS 2022年 10卷 41503-41511页

作者： Zhao, Jingang Weifang Univ Coll Informat & Control Engn Weifang 261061 Shandong Peoples R China

This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, which is actually a fully cooperative game. According to optimal control theory, the HJB equation corresponding to the fully cooperative game is derived. To obtain the solution to HJB equation, a model-based policy iteration (PI) algorithm is first presented. On the basis of the PI algorithm, a DD-ADP algorithm without requiring the system dynamics is developed, and the neural networks (NNs) implementation scheme of the developed DD-ADP algorithm is given. Stability and convergence analysis are derived by Lyapunov theory. Finally, numerical simulation examples on linear and nonlinear multi-controller systems demonstrate the effectiveness of the designed scheme.

关键词： Optimal control Games Mathematical models Control systems Heuristic algorithms dynamic programming Performance analysis adaptive dynamic programming fully cooperative games neural networks multi-controller systems unknown dynamics

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adaptive Optimal Control of CVCF Inverters With Uncertain Load: An adaptive dynamic programming Approach

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IEEE ACCESS 2021年 9卷 89276-89286页

作者： Wang, Zhongyang Yu, Yunjun Fuzhou Inst Technol Sch Appl Sci & Engn Fuzhou 350506 Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Nanchang Univ AI Inst Nanchang 330031 Jiangxi Peoples R China

This paper proposed a data-driven adaptive optimal control approach for CVCF (constant voltage, constant frequency) inverter based on reinforcement learning and adaptive dynamic programming (ADP). Different from existing literature, the load is treated as a dynamic uncertainty and a robust optimal state-feedback controller is proposed. The stability of the inverter-load system has been strictly analyzed. In order to obtain accurate output current differential signal, this paper designs a tracking differentiator. It is ensured that the tracking error asymptotically converges to zero through the proposed output-feedback controllers. A standard proportional integral controller and linear active disturbance rejection control strategy are also designed for the purpose of comparison. The simulation results show that the proposed controller has inherent robustness and does not require retuning with different applications.

关键词： Inverters Voltage control Mathematical model Reinforcement learning Optimal control Load modeling Licenses adaptive optimal control CVCF inverter reinforcement learning adaptive dynamic programming

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Iterative adaptive dynamic programming methods with neural network implementation for multi-player zero-sum games

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NEUROCOMPUTING 2018年 307卷 54-60页

作者： Jiang, He Zhang, Huaguang Han, Ji Zhang, Kun Northeastern Univ Coll Informat Sci & Engn Box 134 Shenyang 110819 Liaoning Peoples R China

This paper presents novel iterative learning methods along with the neural network implementation for multi-player zero-sum games. Solving zero-sum games depends on the solutions of Hamilton-Jacobi-Isaacs equations, which are nonlinear partial differential equations. These solutions are generally difficult or even impossible to be obtained analytically. To overcome this difficulty, iterative adaptive dynamic programming algorithms are utilized. In the related research works, three-network architecture, i.e., critic-actor-disturbance structure, is used to approximate the value function, control policies and disturbance policies. Different from the previous works, this paper employs single-network architecture, i.e., critic-only structure, to implement the proposed algorithms, which reduces the computation burden and the complexity of design procedure. Finally, two simulation examples are provided to illustrate the effectiveness of our proposed methods. (C) 2018 Published by Elsevier B.V.

关键词： adaptive dynamic programming Approximate dynamic programming Zero-sum games Neural networks

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Reactive power control of grid-connected wind farm based on adaptive dynamic programming

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NEUROCOMPUTING 2014年 125卷 125-133页

作者： Tang, Yufei He, Haibo Ni, Zhen Wen, Jinyu Sui, Xianchao Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA Huazhong Univ Sci & Technol Coll Elect & Elect Engn Wuhan 430074 Peoples R China

Optimal control of large-scale wind farm has become a critical issue for the development of renewable energy systems and their integration into the power grid to provide reliable, secure, and efficient electricity. Among many enabling technologies, the latest research results from both the power and energy community and computational intelligence (Cl) community have demonstrated that Cl research could provide key technical innovations into this challenging problem. In this paper, a neural network based controller is presented for the reactive power control of wind farm with doubly fed induction generators (DFIG). Specifically, we investigate the on-line learning and control approach based on adaptive dynamic programming (ADP) for wind farm control and integration with the grid. This controller can effectively dampen the oscillation of the wind farm system after the ground fault of the grid. Compared to previous control strategies, this controller is on-line and "model free", and therefore, can reduce the control complexity. Simulation studies are carried out in Matlab/Simulink and the results demonstrated the effectiveness of the ADP controller. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Computational intelligence adaptive dynamic programming Doubly fed induction generator Wind farm Power system adaptive control

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