Dynamic Event-Triggered Prescribed Performance Control for Partially Unknown Nonlinear System via Adaptive Dynamic Programming

In order to solve the problem of optimal prescribed performance control for unknown dynamic nonlinear systems, an adaptive dynamic programming method based on dynamic event-triggered control strategy is designed. By using Lyapunov stability theory, it is proved that all signals in nonlinear systems are uniformly and ultimately bounded. First, the system under consideration is transformed into an unconstrained system with the prescribed performance by the variable transformation method. Then, the integral reinforcement learning method is used to solve the optimal control problem when the system drift dynamic is unknown. In addition, a dynamic event-triggered control strategy is constructed, which can update the weight estimation and control strategy irregularly, so as to alleviate the problem of excessive data transmission burden when the designed critic neural network approximates the value function. At the same time, Zeno’s behavior in the communication process is avoided. Finally, a numerical example is given to verify the validity of the proposed theory.