Event-Triggered Adaptive Critic Control Design for Discrete-Time Constrained Nonlinear Systems

In this paper, through event-triggered approach, the constrained near-optimal control problem for a class of nonlinear discrete-time systems is investigated and solved by heuristic dynamic programming (HDP) technique. The proposed method can reduce the amount of computation remarkably without deteriorating the system stability. In order to overcome the control constraints and reduce the computational burden, a nonquadratic performance index is introduced. Then, stability analysis of the event-triggered system with control constraints and an event-triggered constrained controller design algorithm are given. Three neural networks are used in the HDP scheme, which are designed to identify the unknown nonlinear system, approximate value function, and control law, respectively. In the model neural network, an effective method is developed to initialize its weights. Finally, two examples are included to demonstrate the present method.