Decentralized Reinforcement Learning Robust Optimal Tracking Control for Time Varying Constrained Reconfigurable Modular Robot Based on ACI and Q-Function

A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI) and state-action value function (Q-function) has been presented to solve the problem of the continuous time nonlinear...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2013; H. 2013; S. 1 - 16
Hauptverfasser: Dong, Bo, Li, Yuanchun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013
John Wiley & Sons, Inc
Schlagworte:
ACI
Constraints
Control algorithms
Control stability
Control theory
Controllers
Decentralized
Decentralized control
Design
Fuzzy logic
Kinematics
Mathematical analysis
Methods
Modular
Neural networks
Nonlinear control
Optimal control
Optimization
Robots
Robust control
Tracking control
Uncertainty
Viscoelasticity
ISSN:1024-123X, 1563-5147
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Zusammenfassung:A novel decentralized reinforcement learning robust optimal tracking control theory for time varying constrained reconfigurable modular robots based on action-critic-identifier (ACI) and state-action value function (Q-function) has been presented to solve the problem of the continuous time nonlinear optimal control policy for strongly coupled uncertainty robotic system. The dynamics of time varying constrained reconfigurable modular robot is described as a synthesis of interconnected subsystem, and continuous time state equation and Q-function have been designed in this paper. Combining with ACI and RBF network, the global uncertainty of the subsystem and the HJB (Hamilton-Jacobi-Bellman) equation have been estimated, where critic-NN and action-NN are used to approximate the optimal Q-function and the optimal control policy, and the identifier is adopted to identify the global uncertainty as well as RBF-NN which is used to update the weights of ACI-NN. On this basis, a novel decentralized robust optimal tracking controller of the subsystem is proposed, so that the subsystem can track the desired trajectory and the tracking error can converge to zero in a finite time. The stability of ACI and the robust optimal tracking controller are confirmed by Lyapunov theory. Finally, comparative simulation examples are presented to illustrate the effectiveness of the proposed ACI and decentralized control theory.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2013/387817