UKF-Based Multistep Heuristic Dynamic Programming for Optimal Event-Triggering Control of Nonlinear Systems With Asymmetric Input Constraints

Abstract

In this article, an unscented Kalman filter (UKF)-based multistep heuristic dynamic programming (MsHDP) optimal control algorithm is developed for nonlinear discrete-time (DT) systems with uncertainty and asymmetric input constraints. The Hamilton–Jacobi–Bellman (HJB) equation is solved by the UKF-based MsHDP algorithm, which has the advantages of faster convergence speed and handling unknown disturbances in the system. The convergence of the developed algorithm is proved under certain conditions, and the system stability is guaranteed. To reduce the communication needs, a dynamic event-triggering mechanism is designed. Then, an event-based estimation-critic structure is proposed to implement the UKF-based MsHDP algorithm, where the UKF is used to estimate the future state of uncertain systems and the critic neural network (NN) is used to approximate cost function. Finally, simulation results are provided to verify the effectiveness of the developed algorithm.