Observed-based event-triggered control for nonlinear systems with disturbances using adaptive dynamic programming
Chuanhao Hu, Yuanyuan Zou, Shaoyuan Li · 2020
This paper proposes an observer-based event-triggered adaptive dynamic programming (ADP) approach for the nonlinear system with unknown disturbances to efficiently reduce computational cost. Under the event-triggered mechanism, an observer is constructed to identify unknown disturbances and ensure the estimation error to be ultimately uniformly bounded(UUB). Next, an adaptive triggering condition related to the estimation error of disturbances is derived with the help of a modified performance index function. Then, the event-based control algorithm is implemented based on a single critic neural network(NN) structure to approximate the optimal control law. Moreover, the stability analysis for the closed-loop system is presented with the event-driven control law and the weight estimated error of the critic NN is proved to be UUB. Finally, simulation results illustrate the effectiveness of the proposed control scheme. Compared with the traditional ADP approach, the proposed event-triggered strategy can reduce controller updates with guaranteed performance of the system.