Event-triggered Multi-agent Optimal Regulation Using Adaptive Dynamic Programming
Event-triggered Multi-agent Optimal Regulation Using Adaptive Dynamic Programming
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DOI：
10.1109/ijcnn48605.2020.9207205
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Xiangnan Zhong;Haibo He
中科院分区：
文献类型：
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作者：
Xiangnan Zhong;Haibo He
本文开发了一种基于自适应动态编程（ADP）技术的事件触发的多代理控制方法。样本的速度是由自适应触发条件确定的，以确保事件触发的学习过程的稳定性。使所有代理与领导者的动力学同步，并同时到达NASH等效的情况下，提出的方法可以在学习过程中保存计算资源。演示开发方法的性能。
This paper develops an event-triggered multi-agent control method based on adaptive dynamic programming (ADP) techniques. Different from the traditional ADP-based multi-agent control with fixed sampling period, our method designs an adaptive controller only based on the efficiently reduced samples. The sampling instants are decided by an adaptive triggering condition to guarantee the stability of the event-triggered learning process. The theoretical analysis of the proposed method is also provided in this paper. It is proved that the designed event-triggered ADP controller can make all the agents synchronize to the leader’s dynamics with reduced sampled data, and also reach Nash equilibrium at the same time. Therefore, the proposed method can save the computational resources in the learning process. Finally, the simulation results verify the theoretical analysis and also demonstrate the performance of the developed method.