An Event-Triggered Heuristic Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems

摘要

Event-triggered control means the control law of the systems will only be updated when the triggering condition is met, so that the computational burden is reduced. In this paper, a new triggering condition of the heuristic dynamic programming (HDP) algorithm is developed for discrete-time nonlinear systems. Two neural networks are constructed to estimate the value function and the control law. Besides, the Lyapunov stability of systems under the algorithm is proven. Finally, an example is presented to show the effectiveness of the algorithm.