Multi-event-triggered adaptive dynamic programming for non-zero-sum game of unknown nonlinear system

In this article, a multi-event-triggered (MET) near-optimal tracking control algorithm is developed for a discrete-time (DT) nonlinear non-zero-sum game (NZSG). Firstly, the iterative dual heuristic dynamic programming (DHP) approach is utilized to obtain the optimal tracking control set. Subsequently, distinct independent triggering conditions are formulated for control inputs to improve the utilization efficiency of resources and ensure that the independence between them and the closed-loop system has an excellent control performance. Meanwhile, input-to-state stability (ISS) is proven for the MET tracking plant. Additionally, neural networks (NNs) are designed in the iterative DHP algorithm to construct the model module, the critic module and the action modules, with aims to identify the unknown system, approximate costate function and estimate optimal tracking control strategy set, respectively. Finally, two numerical experimental simulations are conducted to verify the effectiveness of the proposed scheme.