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检索条件"主题词=Adaptive Dynamic Programming"

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Improvement of LMI controllers of Takagi-Sugeno models via Q-learning

Improvement of LMI controllers of Takagi-Sugeno models via Q...

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4th IFAC Conference on Intelligent Control and Automation Sciences (ICONS)

作者： Diaz, Henry Armesto, Leopoldo Sala, Antonio Univ Politecn Valencia IDF Inst Univ Autom Inf Ind AI2 C Camino Vera S-N E-46022 Valencia Spain

This paper presents a preliminary attempt to bridge the conservative (shape-independent) results from guaranteed-cost LMIs and the reinforcement learning setups which learn optimal controllers from data. In this sense, the proposed approach uses an initialization based on the LMI solution and proposes an approximation of the Q-function using polynomials of the membership functions in Takagi-Sugeno models. The resulting controller is shape-dependent, that is, uses the knowledge of membership functions and data to clearly improve LMI solutions. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Reinforcement learning adaptive dynamic programming Q-learning Takagi-Sugeno LMI

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Decentralized Tracking Control of Interconnected Nonlinear Systems with Unmatched External Disturbances 43

Decentralized Tracking Control of Interconnected Nonlinear S...

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43rd Chinese Control Conference (CCC)

作者： Yuan, Zeqiang Wang, Ding Liu, Ao Hu, Qinna Zhou, Zihang 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 Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

ISBN: (纸本)9798350366907;9789887581581

In this paper, we develop a decentralized tracking control (DTC) strategy to stabilize a class of continuous-time nonlinear interconnected large-scale systems in the presence of unmatched external disturbances. This strategy is derived from an online learning approach of optimal control. Initially, the DTC problem is formulated to ensure both tracking control of isolated subsystems and the overall stability of the large-scale system. By combining the tracking error with the reference trajectory, some augmented systems are constructed and then the DTC design is transformed into an optimal control problem. Considering external disturbances, we formulated it as a two-player zero-sum game. Critic neural networks are utilized to solve the Hamilton-Jacobi-Isaacs equation. This approach allows for the estimation of Nash equilibrium solution that encompasses both the optimal control law and the worst-case disturbance law. Notably, a novel gradient descent strategy with momentum is established to tune the weights of the critic neural network for approximating the cost function better. Finally, an experimental simulation is provided to verify the effectiveness of the developed DTC scheme.

关键词： adaptive dynamic programming Decentralized control H-infinity control Reinforcement learning Tracking control

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Constrained Robust Optimal Sliding Mode Control for Uncertain Nonlinear Systems Using ADP Approach 6

Constrained Robust Optimal Sliding Mode Control for Uncertai...

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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Qiuxia Qu Huaguang Zhang Rui Yu Geyang Xiao School of Information Science and Engineering Northeastern University

ISBN: (纸本)9781509054626

In this paper, a novel constrained composite sliding mode controller is studied for uncertain nonlinear systems with input saturation. Based on integral sliding mode and approximate dynamic programming theory, the proposed sliding mode control consists of discontinuous control used for completely compensating matched uncertainties and continuous control that results in an optimal sliding mode dynamics. Without using linearization techniques, the constrained optimal control law for nominal nonlinear systems is approximated by using an online approximate learning algorithm with actor-critic NN *** techniques are used to demonstrate the uniform ultimate bounded convergence condition for closed-loop nominal system and the weight errors.

关键词： Sliding mode control Optimal control adaptive dynamic programming Nonlinear systems Input saturation

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Optimal Impulsive Control for Discrete Stochastic Systems Through ADP 41

Optimal Impulsive Control for Discrete Stochastic Systems Th...

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第41届中国控制会议

作者： Mingming Liang Derong Liu School of Automation Guangdong University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper constructs a novel general impulsive transition matrix in order to reveal the impulsive dynamics of the stochastic systems across the impulsive "events".On the foundation of this matrix,the policy iteration based impulsive adaptive dynamic programming(IADP) algorithm and its more efficient version,are developed to obtain the optimal impulsive controller design scheme for stochastic *** and optimality analysis are provided to show that both the IADP and the efficient IADP(EIADP) algorithms can find the global optimal performance index *** proposed EIADP algorithm updates the iterative policies partially at each iteration step according to the actual hardware constraints,enabling the ADP-based algorithms to run on computing devices with limited memory ***,a simulation experiment justifies the theoretical conjectures.

关键词： adaptive dynamic programming policy iteration stochastic systems transition matrix optimal control

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Learning-Based Tracking Control of Unknown Robot Systems With Online Parameter Estimation

Learning-Based Tracking Control of Unknown Robot Systems Wit...

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American Control Conference (ACC)

作者： Peng, Zhinan Chen, Chen Luo, Rui Zhang, Jingting Cheng, Hong Ghosh, Bijoy Kumar Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

ISBN: (纸本)9798350382662;9798350382655

This paper proposes a novel learning-based optimal control approach for the tracking control problem of a robot manipulator, which is allowed to have uncertainty in the model parameters. We first employ neural network technique to design an identifier for the system parameters estimation. Then, an optimal tracking controller is proposed with a critic network. A novel online weight adaptation law is designed with the dynamic regression extension and mixing technique, for updating both the unknown parameters and the critic network's weight during the control process. With such setup, our approach can develop an important capability of relaxing the persistent excitation condition, leading to improved parameter-convergence accuracy and control applicability, which can be distinguished from the existing methods that use gradient-descent based weight adaptation laws. Rigorous theoretical analysis is conducted based on the Lyapunov stability theory and demonstrates the the uniform ultimate boundedness stability of the closed-loop systems. Effectiveness of the proposed method is validated through simulation study by using a two degree-of-freedom robot system.

关键词： Optimal tracking control adaptive dynamic programming dynamic regression extension and mixing Parameter identification Robot systems

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Decentralized Stabilization for Nonlinear Systems with Unknown Mismatched Interconnections

Decentralized Stabilization for Nonlinear Systems with Unkno...

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23rd International Conference on Neural Information Processing (ICONIP)

作者： Zhao, Bo Wang, Ding Shi, Guang Liu, Derong Li, Yuanchun 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 Changchun Univ Technol Dept Control Sci & Engn Changchun 130012 Peoples R China

ISBN: (纸本)9783319466750;9783319466743

This paper establishes a neural network and policy iteration based decentralized control scheme to stabilize large-scale nonlinear systems with unknown mismatched interconnections. For relaxing the common assumption of upper boundedness on interconnections when designing the decentralized optimal control, interconnections are approximated by neural networks with local signals of isolated subsystem and replaced reference signals of coupled subsystems. By using the adaptive estimation term, the performance index function is constructed to reflect the replacement error. Hereafter, it is proven that the developed decentralized optimal control policies can guarantee the closed-loop large-scale nonlinear system to be uniformly ultimately bounded. The effectiveness of the developed scheme is verified by a simulation example.

关键词： adaptive dynamic programming Decentralized control Unknown mismatched interconnections Policy iteration

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Optimal Tracking Control of Heterogeneous Multi-agent Systems with Switching Topology via Actor-Critic Neural Networks 37

Optimal Tracking Control of Heterogeneous Multi-agent System...

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37th Chinese Control Conference (CCC)

作者： Peng, Zhinan Hu, Jiangping Ghosh, Bijoy K. Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Sichuan Peoples R China Texas Tech Univ Dept Math & Stat Lubbock TX 79409 USA

ISBN: (纸本)9789881563958

In this paper, an optimal tracking control problem is solved for high-order heterogeneous multi-agent systems with time-varying interaction networks within the framework of reinforcement learning. First, the optimal tracking control problem is formulated as a leader-follower multi-agent system. Second, a policy iteration based adaptive dynamic programming (ADP) algorithm is proposed to compute the performance index and the control law. Furthermore, the convergence to the optimal solutions is analyzed for the proposed algorithm. Third, an actor-critic neural network is applied to approximate the iterative performance index function and the control law, which implement the policy iteration algorithm online without using the knowledge of the system dynamics. Finally, some simulation results are presented to demonstrate the proposed optimal tracking control strategy.

关键词： Optimal tracking control multi-agent systems adaptive dynamic programming actor-critic neural network

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Cooperative Collision Avoidance for Multi-Vehicle Systems Using Reinforcement Learning

Cooperative Collision Avoidance for Multi-Vehicle Systems Us...

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18th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Wang, Qichen Phillips, Chris Queen Mary Univ London Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9781467355087;9781467355063

Collision avoidance is a fundamental problem in navigation. In this paper, we present a novel method of cooperative movement planning to examine how two vehicles can orchestrate their movements so as to avoid collisions and subsequently return to their intended paths. Movement planning in this research is solved by regarding it as a decision process. When the vehicles are at risk of a collision, the system determines appropriate steering motions for both vehicles at each time step, so that they can cooperatively change course to avoid collisions and return to their original course when the risk is averted. Reinforcement learning is applied to solve this decision-making task. States of the system are described in terms of the vehicles' position and orientation and actions are defined considering the kinematic constraints of the vehicles. In reinforcement learning, an approximate value function is iteratively developed according to certain rules to evaluate state-action combinations of the system. Appropriate motions are selected by the system after calculating the approximate value of possible target states, which also satisfy the requirement of the smoothness of paths, as well as the distances between, and velocities of, both vehicles. The method of least squares is applied in the iterative mechanism to update the approximate value function given a scoring technique for a collection of state samples featuring continuous state space and action space. This paper summarizes the concept and methodologies used to implement an online cooperative collision avoidance system. Different scenarios are tested to assess the performance of the proposed algorithm.

关键词： Cooperative Path Planning Collision Avoidance Reinforcement Learning adaptive dynamic programming

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Integral Reinforcement Learning-Based Optimal Control for Nonzero-Sum Games of Multi-Player Input-Constrained Nonlinear Systems 7

Integral Reinforcement Learning-Based Optimal Control for No...

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7th International Conference on Robotics and Artificial Intelligence (ICRAI)

作者： Wu, Qiuye Zhao, Bo Liu, Derong Guangdong Univ Technol Guangzhou Guangdong Peoples R China Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9781450385855

This paper investigates an integral reinforcement learning (IRL)based optimal control scheme to solve nonzero-sum games of multiplayer input-constrained nonlinear systems with unknown drift dynamics. The IRL method is introduced to obviate the identification procedure of the unknown drift dynamics. In order to achieve Nash equilibrium for each player, the simplified optimal control policy for each player is obtained by solving the coupled Hamilton-Jacobi equation with the critic neural network only. Thus, the computational resource is saved and the control structure is easy to realize. The closed-loop system is guaranteed to be uniformly ultimately bounded based on the Lyapunov stability analysis. Simulation results illustrate the effectiveness of the developed control scheme.

关键词： adaptive dynamic programming integral reinforcement learning neural networks nonzero-sum games input constraints

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A neuro-optimal control method of modular robot manipulators based on nonzero-sum game strategy

A neuro-optimal control method of modular robot manipulators...

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Chinese Automation Congress (CAC)

作者： Dong, Bo Chen, Jingchen Wang, Yuexi Ma, Bing Liu, Guangjun Li, Yuanchun Changchun Univ Technol Dept Control Sci & Engn Changchun Jilin Peoples R China Ryerson Univ Dept Aerosp Engn Toronto ON Canada

ISBN: (纸本)9781728176871

This paper presents a nonzero-sum strategy-based nenro-optimal control method for modular robot manipulators (MRMs). Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems. 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 nonzero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approzimator is developed for solving the Hamilton-Jacobi (W) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. Finally, experiment results illustrated the effectiveness of the developed control method.

关键词： modular robot manipulators adaptive dynamic programming nonzero-sum differential game optimal control

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