arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2202.02547math.OC
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事件触发的动态未知多刚体网络最优姿态一致性

Event-Triggered Optimal Attitude Consensus of Multiple Rigid Body Networks with Unknown Dynamics

Xin Jin, Shuai Mao, Ljupco Kocarev, Chen Liang, Saiwei Wang, Yang Tang

摘要:

针对动态未知的多刚体网络的最优姿态一致性问题,提出了一种事件触发的强化学习方法。首先,通过姿态动力学构造一致性误差。根据Bellman最优性原理,得到了最优控制器的隐式形式和相应的Hamilton-Jacobi-Bellman(HJB)方程。由于增广系统的存在,可以不依赖于系统的动态特性而直接得到最优控制器。其次,采用自触发机制,减少了控制器更新时的计算和通信负担。为了解决HJB方程难以解析求解的问题,提出了一种只需要事件触发时刻测量数据的RL方法。对于每个智能体,只设计一个神经网络来逼近最优值函数。每个神经网络仅在事件触发的时刻更新。同时,得到了闭环系统的一致最终有界性,避免了Zeno行为。最后,在多刚体网络上的仿真结果验证了该方法的有效性。

英文摘要:

In this paper, an event-triggered Reinforcement Learning (RL) method is proposed for the optimal attitude consensus of multiple rigid body networks with unknown dynamics. Firstly, the consensus error is constructed through the attitude dynamics. According to the Bellman optimality principle, the implicit form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman (HJB) equations are obtained. Because of the augmented system, the optimal controller can be obtained directly without relying on the system dynamics. Secondly, the self-triggered mechanism is applied to reduce the computing and communication burden when updating the controller. In order to address the problem that the HJB equations are difficult to solve analytically, a RL method which only requires measurement data at the event-triggered instants is proposed. For each agent, only one neural network is designed to approximate the optimal value function. Each neural network is updated only at the event triggered instants. Meanwhile, the Uniformly Ultimately Bounded (UUB) of the closed-loop system is obtained, and the Zeno behavior is also avoided. Finally, the simulation results on a multiple rigid body network demonstrate the validity of the proposed method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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