Constrained Event-Triggered H∞ Control Based on Adaptive Dynamic Programming With Concurrent Learning

In this article, an event-triggered
$H_{\infty }$
control method is proposed based on adaptive dynamic programming (ADP) with concurrent learning for unknown continuous-time nonlinear systems with control constraints. First, a system identification technique based on neural networks (NNs) is adopted to identify completely unknown systems. Second, a critic NN is employed to approximate the value function. A novel weight updating rule is developed based on the event-triggered control law and time-triggered disturbance law, which reduces controller execution times and guarantees the stability of the system. Subsequently, concurrent learning is applied to the weight updating rule to relax the demand for the traditional persistence of excitation condition that is difficult to implement online. Finally, the comparison between the time-triggered method and event-triggered method in simulation demonstrates the effectiveness of the developed constrained event-triggered ADP method.