Event-triggered adaptive integral reinforcement learning method for zero-sum differential games of nonlinear systems with incomplete known dynamics.

This paper designs a novel event-based adaptive learning method for solving zero-sum games (ZSGs) of nonlinear systems with incomplete known dynamics. Firstly, a discounted cost is introduced for the system with nonzero equilibrium point to obtain the near-optimal strategy pair. Then, the employment of integral reinforcement learning (IRL) makes it unnecessary to acquire the model of the drift dynamics. To approximate the solution of the Hamilton-Jacobi-Isaacs equations (HJIEs), single-critic network is constructed with the modified tuning law utilizing preprocessed data. For purpose of increasing algorithm efficiency, the event-triggered mechanism (ETM) is introduced which could obviate Zeno behavior. Furthermore, the state and critic weight vector error are proved to be uniform ultimate bounded (UUB) through Lyapunov approach. Finally, the effectiveness of the proposed method is validated by conducting a simulation experiment. [ABSTRACT FROM AUTHOR]