Event-Triggered Constrained \(H_\infty \) Control Using Concurrent Learning and ADP

In this paper, an optimal control algorithm based on concurrent learning and adaptive dynamic programming for event-triggered constrained \(H_\infty \) control is developed. First, the \(H_\infty \) control system under consideration is based on event-triggered constrained input and time-triggered external disturbance, which saves resources and reduces the network bandwidth burden. Second, in the implementation of the control scheme, a critic neural network is designed to approximate unknown value function. Moreover, concurrent learning techniques participate in weight training, making the implementation process simple and effective. Lastly, the stability of the system and the effectiveness of the algorithm are demonstrated through theorem proofs and simulation results.