Distributed Optimal Tracking Control of Discrete-Time Multiagent Systems via Event-Triggered Reinforcement Learning

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 69; no. 9; pp. 3689 - 3700
• Main Authors: Peng, Zhinan, Luo, Rui, Hu, Jiangping, Shi, Kaibo, Ghosh, Bijoy Kumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: actor-critic neural networks; Control systems design; Controllers; Convergence; Directed graphs; Discrete time systems; event-triggered mechanism; Learning; Multi-agent systems; Multiagent systems; Neural networks; Optimal control; Optimal tracking control; Performance indices; Reinforcement learning; Stability analysis; Tracking control; Tuning

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. In contrast to traditional reinforcement learning-based methods for optimal coordination and control of multi-agent systems with a time-triggered control mech...