Event-based Integral Reinforcement Learning Algorithm for Non-zero-sum Games of Partially Unknown Nonlinear Systems
Hanguang Su, Huaguang Zhang, Yanhong Luo, Qiuye Sun · 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) · 2021
In this work, a novel event-based integral reinforcement learning (IRL) adaptive control method is developed to solve the multiplayer non-zero-sum (NZS) games of the nonlinear systems with unknown drift dynamics. By virtue of the IRL algorithm, the system drift dynamics is no more needed in the controller design. Moreover, different from the existing iteration computation methods, this method is online implemented, on which condition the event-triggered control framework can be combined with the IRL algorithm in solving the NZS game problems. In this method, a state-dependent triggering condition is proposed, thus the computation and communication loads are reduced in the control process. Moreover, the uniform ultimate boundedness (UUB) stability of the controlled system and the convergence of the critic weights have also been proved. Finally, a numerical example is provided to demonstrate the effectiveness of our method.