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Optimal output synchronization of heterogeneous multi-agent systems using measured input-output data

INFORMATION SCIENCES 2022年 582卷 462-479页

作者： Liu, Yingying Wang, Zhanshan Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

In this paper, the optimal output synchronization of heterogeneous multi-agent systems is studied. To overcome the shortcoming of previous methods that require system model, a data-based optimal control policy is proposed for the output synchronization problem. Besides, the intrinsic equivalence relationship between model-based optimal solution and the proposed data-based optimal solution is proved. To solve the proposed control policy, a reinforcement learning algorithm based on measured input-output data is presented, which is different from existing model-free algorithms based on internal state. According to the algorithm, the optimal output synchronization problem of heterogeneous multi agent systems can be solved when the full-state vector is unavailable. Finally, the effectiveness of proposed algorithm is verified by simulation examples. (c) 2021 Published by Elsevier Inc.

关键词： output synchronization Multi-agent systems Reinforcement learning measured input-output data

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Event-Triggered Adaptive Dynamic Programming for Continuous-time Nonlinear System Using measured input-output data

Event-Triggered Adaptive Dynamic Programming for Continuous-...

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International Joint Conference on Neural Networks

作者： Xiangnan Zhong Zhen Ni Haibo He Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI USA

ISBN: (纸本)9781479919611

In this paper, we propose a novel event-triggered adaptive dynamic programming (ADP) method using only the input-output data. Event-triggered method is widely used for its computational efficiency capacity. Comparing with the traditional method which updates the controller periodically, the event-triggered method only updates the controller when it is necessary and therefore the computation is reduced. Generally, the triggered condition is based on the system current and sampled states. In this paper, we consider a neural-network-based observer to recover the system dynamics using the measured input-output data. The triggered instants are calculated according to the recovered state. Stability analysis of the proposed approach is presented. We verify our proposed method through a robot-arm example.

关键词： Event-Triggered Adaptive Dynamic Programming Continuous-time Nonlinear System measured input-output data

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