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Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3441-3458页

作者： Dong, Bo Zhou, Fan Liu, Keping Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a critic-identifier structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control issue, which consists of model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is deployed for each joint module where the local dynamic information is utilized to design the model compensation controller. A neural network (NN) identifier is established to approximate the interconnected dynamic coupling. Based on ADP and local online policy iteration algorithm, the Hamiltonian-Jacobi-Bellman equation is solved by constructing a critic NN, and then the approximate optimal control policy derivation is possible. The closed-loop robotic system is asymptotic stable by the implementation of a set of developed decentralized control policies. Simulations are presented to demonstrate the effectiveness of the proposed method.

关键词： Modular robot manipulator Decentralized control adaptive dynamic programming Optimal control Neural network Interconnected dynamic coupling

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Constrained Decoupling adaptive dynamic programming for A Partially Uncontrollable Time-Delayed Model of Energy Systems

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INFORMATION SCIENCES 2022年 608卷 1352-1374页

作者： Chen, Zitao Chen, Si-Zhe Chen, Kairui Zhang, Yun Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

The lack of feedback-loop design and uncontrollable state reconstruction, such as load demand and renewable generation, are key factors to hinder the development of energy control. adaptive dynamic programming is an efficient method to break through the bot-tleneck. However, most current works assume that the evolution of uncontrollable state satisfies Markovian property. This paper proposes a time-delayed adaptive dynamic pro-gramming framework for optimal energy control, utilizing more comprehensive historical data for state reconstruction. The uncontrollable state is reconstructed by an attention mechanism-based Transformer network based on online measured data. Then, a self -learning iterative algorithm is developed to obtain the optimal state feedback control policy under a discounted performance index function. The problem of solving iterative control policy, which is a mixed-integer nonlinear programming problem with an enor-mous computational burden, is decomposed into three sub-problems, i.e. nonlinear pro-gramming with no discrete variable, which is solved by the Projected Newton method, mixed-integer quadratic programming, and quadratic programming. Numerical results illustrate that the developed control algorithm minimizes the electricity cost in the long term and avoids peak load.(c) 2022 Elsevier Inc. All rights reserved.

关键词： adaptive dynamic programming intelligent control energy control Transformer network smart buildings

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Nonlinear trajectory-tracking control for autonomous underwater vehicle based on iterative adaptive dynamic programming

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第3期37卷 4205-4215页

作者： Che, Gaofeng Liu, Lijun Yu, Zhen Xiamen Univ Sch Aerosp Engn Xiamen 361102 Fujian Peoples R China

In this paper, an iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle. Two iteration procedures are used in the method, which are the i-iteration and the *** i-iteration aims to obtain iterative trajectory-tracking control laws and the j-iteration aims to obtain iterative value functions in the i-iteration. The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. The policy iteration algorithm is the interacting policy and value iteration algorithms. The neural networks are used to realize the proposed algorithm and the convergence and optimality properties of the proposed algorithm are analysed. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

关键词： adaptive dynamic programming autonomous underwater vehicle discrete-time neural network trajectory-tracking control

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Dimension reduction based adaptive dynamic programming for optimal control of discrete-time nonlinear control-affine systems

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INTERNATIONAL JOURNAL OF CONTROL 2023年第11期96卷 2799-2811页

作者： Li, Qiang Xu, Yunjun Univ Cent Florida Dept Mech & Aerosp Engn 4000 Cent Florida Blvd Orlando FL 32826 USA

dynamic programming based methods have been widely used in solving discrete-time nonlinear constrained optimal control problems. However, applying these methods in real-time is challenging because a large amount of memory is needed and the associated computational cost is high. Here, a search space dimension reduction strategy is proposed for a class of nonlinear discrete-time systems that are control-affine and invertible. Specifically, a bio-inspired motion rule is combined with inverse dynamics to reduce the value iteration search space to one dimension. The corresponding suboptimal control algorithm is developed and its optimality is analysed. An adaptation rule is developed to estimate uncertainties and improve the base policy. The closed-loop system is proven to be asymptotically stable. The advantages of the algorithm including much smaller computational cost and significantly reduced memory usage are demonstrated with two simulation examples.

关键词： Nonlinear constrained optimal control adaptive dynamic programming real-time control control-affine system uncertainty

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Multi-objective optimal control for a class of nonlinear time-delay systems via adaptive dynamic programming

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SOFT COMPUTING 2013年第11期17卷 2109-2115页

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

A novel multi-objective adaptive dynamic programming (ADP) method is constructed to obtain the optimal controller of a class of nonlinear time-delay systems in this paper. Using the weighted sum technology, the original multi-objective optimal control problem is transformed to the single one. An ADP method is established for nonlinear time-delay systems to solve the optimal control problem. To demonstrate that the presented iterative performance index function sequence is convergent and the closed-loop system is asymptotically stable, the convergence analysis is also given. The neural networks are used to get the approximative control policy and the approximative performance index function, respectively. Two simulation examples are presented to illustrate the performance of the presented optimal control method.

关键词： Optimal control adaptive dynamic programming Multi-objective Approximate dynamic programming Time-delay

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Invariant adaptive dynamic programming for Discrete-Time Optimal Control

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第11期50卷 3959-3971页

作者： Zhu, Yuanheng Zhao, Dongbin He, Haibo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Rhode Isl Dept Elect Comp & Biomed Engn Kingston RI 02881 USA

For systems that can only be locally stabilized, control laws and their effective regions are both important. In this paper, invariant policy iteration is proposed to solve the optimal control of discrete-time systems. At each iteration, a given policy is evaluated in its invariantly admissible region, and a new policy and a new region are updated for the next iteration. Theoretical analysis shows the method is regionally convergent to the optimal value and the optimal policy. Combined with sum-of-squares polynomials, the method is able to achieve the near-optimal control of a class of discrete-time systems. An invariant adaptive dynamic programming algorithm is developed to extend the method to scenarios where system dynamics is not available. Online data are utilized to learn the near-optimal policy and the invariantly admissible region. Simulated experiments verify the effectiveness of our method.

关键词： Optimal control Discrete-time systems Heuristic algorithms dynamic programming Convergence Artificial intelligence Nonlinear systems adaptive dynamic programming discrete-time systems invariant admissibility optimal control policy iteration sum of squares

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Neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming

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INFORMATION SCIENCES 2014年 282卷 167-179页

作者： Wang, Ding Liu, Derong Li, Hongliang Ma, Hongwen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, the neural-network-based robust optimal control design for a class of uncertain nonlinear systems via adaptive dynamic programming approach is investigated. First, the robust controller of the original uncertain system is derived by adding a feedback gain to the optimal controller of the nominal system. It is also shown that this robust controller can achieve optimality under a specified cost function, which serves as the basic idea of the robust optimal control design. Then, a critic network is constructed to solve the Hamilton-Jacobi-Bellman equation corresponding to the nominal system, where an additional stabilizing term is introduced to verify the stability. The uniform ultimate boundedness of the closed-loop system is also proved by using the Lyapunov approach. Moreover, the obtained results are extended to solve decentralized optimal control problem of continuous-time nonlinear interconnected large-scale systems. Finally, two simulation examples are presented to illustrate the effectiveness of the established control scheme. (C) 2014 Elsevier Inc. All rights reserved.

关键词： adaptive dynamic programming Approximate dynamic programming Decentralized optimal control Neural network Robust optimal control

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Robust differential game guidance laws design for uncertain interceptor-target engagement via adaptive dynamic programming

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INTERNATIONAL JOURNAL OF CONTROL 2017年第5期90卷 990-1004页

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

In this paper, the problem of intercepting a maneuvering target is formulated as a two-player zero-sum differential game framework affected by matched uncertainties. By introducing an appropriate cost function that reflects the uncertainties, the robust control is transformed into a two-player zero-sum differential game control problem and therefore ensures the compensation of the matched uncertainties. Additionally, the corresponding Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing a critic neural network (NN). The closed-loop system and the critic NN weight estimation error are proved to be uniform ultimate boundedness (UUB) by utilising Lyapunov approach. Finally, the effectiveness of the proposed robust guidance law is demonstrated by using a nonlinear two-dimensional kinematics, assuming first-order dynamics for the interceptor and the target.

关键词： Robust differential game guidance law adaptive dynamic programming uncertainties

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Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

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AUTOMATICA 2012年第8期48卷 1825-1832页

作者： Wang, Ding Liu, Derong Wei, Qinglai Zhao, Dongbin Jin, Ning Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： adaptive critic designs adaptive dynamic programming Approximate dynamic programming Globalized dual heuristic programming Intelligent control Neural network Optimal control

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Optimal tracking control of a class of nonlinear discrete-time switched systems using adaptive dynamic programming

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期24卷 531-538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, an infinite-horizon optimal tracking control scheme is proposed for a class of nonlinear discrete-time switched systems. First, via system transformation, the optimal tracking problem is converted into designing an optimal regulator for the tracking error dynamics. And then, with convergence analysis in terms of value function and control policy, the iterative adaptive dynamic programming (ADP) algorithm is introduced to obtain the infinite-horizon optimal tracking controller which makes the value function close to its optimal value function. Next, two neural networks are used as parametric structures to implement the ADP algorithm, which aim at approximating the value function and the control policy, respectively. Finally, a simulation example is included to complement the theoretical discussions.

关键词： Nonlinear switched system Optimal tracking control adaptive dynamic programming Neural network

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