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Optimized Multi-Agent Formation Control Based on Identifier-Actor-Critic Reinforcement Learning Algorithm
IEEE Transactions on Fuzzy Systems ( IF 11.9 ) Pub Date : 2018-10-01 , DOI: 10.1109/tfuzz.2017.2787561
Guoxing Wen , C. L. Philip Chen , Jun Feng , Ning Zhou

The paper proposes an optimized leader–follower formation control for the multi-agent systems with unknown nonlinear dynamics. Usually, optimal control is designed based on the solution of the Hamilton–Jacobi–Bellman equation, but it is very difficult to solve the equation because of the unknown dynamic and inherent nonlinearity. Specifically, to multi-agent systems, it will become more complicated owing to the state coupling problem in control design. In order to achieve the optimized control, the reinforcement learning algorithm of the identifier–actor–critic architecture is implemented based on fuzzy logic system (FLS) approximators. The identifier is designed for estimating the unknown multi-agent dynamics; the actor and critic FLSs are constructed for executing control behavior and evaluating control performance, respectively. According to Lyapunov stability theory, it is proven that the desired optimizing performance can be arrived. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach.

中文翻译:

基于Identifier-Actor-Critic强化学习算法的优化多Agent编队控制

该论文针对非线性动力学未知的多智能体系统提出了一种优化的leader-follower编队控制。通常,最优控制是基于Hamilton-Jacobi-Bellman方程的解来设计的,但由于未知的动力学和固有的非线性,求解该方程非常困难。具体来说,对于多智能体系统,由于控制设计中的状态耦合问题,它会变得更加复杂。为了实现优化控制,基于模糊逻辑系统(FLS)逼近器实现了标识符-演员-评论家架构的强化学习算法。标识符用于估计未知的多智能体动态;演员和评论家 FLS 分别用于执行控制行为和评估控制性能。根据李雅普诺夫稳定性理论,证明可以达到期望的优化性能。最后,通过仿真实例进一步证明了所提出的控制方法的有效性。
更新日期:2018-10-01
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