LAUSR

In this paper, an event-triggered neural critic learning algorithm is investigated to address constrained nonzero-sum game problems with discrete-time nonaffine dynamics. First, in order to ensure the saturation independence of two controllers in the nonzero-sum game problem, we adopt two different boundaries to constrain them respectively. Then, a novel triggering condition is designed to reduce the update times of the controllers, which achieves the purpose of less calculation. It is emphasised that the triggering condition is established based on the iteration of the time-triggered mechanism. Meanwhile, we prove that the real cost function possesses a predetermined upper bound, which realises the cost guarantee of the controlled system. In addition, we prove that the closed-loop system using the developed algorithm is asymptotically stable and that the system state and the sampling state are uniformly ultimately bounded during the process of training neural networks. Finally, two simulation examples are conducted to demonstrate the effectiveness of the proposed algorithm.