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Article Abstract
This paper addresses the tracking control problem of nonlinear discrete-time multi-agent systems (MASs). First, a local neighborhood error system (LNES) is constructed. Then, a novel tracking algorithm based on asynchronous iterative Q-learning (AIQL) is developed, which can transform the tracking problem into the optimal regulation of LNES. The AIQL-based algorithm has two Q values Q and Q for each agent i, where Q is used for improving the control policy and Q is used for evaluating the value of the control policy. Moreover, the convergence of LNES is given. It is shown that the LNES converges to 0 and the tracking problem is solved. A neural network-based actor-critic framework is used to implement AIQL. The critic network of AIQL is composed of two neural networks, which are used for approximating Q and Q respectively. Finally, simulation results are given to verify the performance of the developed algorithm. It is shown that the AIQL-based tracking algorithm has a lower cost value and faster convergence speed than the IQL-based tracking algorithm.
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| http://dx.doi.org/10.1016/j.neunet.2024.106667 | DOI Listing |
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