A Tracking Control Method based on Event-Triggered Adaptive Dynamic Programming
Ziyang Wang, Qinglai Wei, Derong Liu · 2019
In this paper, an event-triggered adaptive dynamic programming (ADP) algorithm is developed to solve optimal tracking control problems. The event-triggered control law will be updated only when the triggering conditions are satisfied. Therefore, the computational cost can be reduced and the celerity of tracking can be improved. Compared to existing works, novel triggering conditions are designed in this paper. Besides, the stability is guaranteed with less assumption. Neural networks are used to implement the tracking control algorithm. Finally, an example is employed to show the effectiveness of the algorithm.