Predictive event-triggered control based on heuristic dynamic programming for nonlinear continuous-time systems
Date of Original Version
In this paper, a novel predictive event-triggered control method based on heuristic dynamic programming (HDP) algorithm is developed for nonlinear continuous-time systems. A model network is used to estimate the system state vector, so that the event-triggered instant is available to predict one step ahead of time. Furthermore, an actor-critic structure is used to approximate the optimal event-triggered control law and performance index function. Although event-triggered adaptive dynamic programming (ADP) has been investigated in the community before, to our best knowledge, this is the first study of using a 'predictive' approach through a model network to design the event-triggered ADP. This is the key contribution of this work. Compared to the existing event-triggered ADP methods, our simulations demonstrate that the predictive event-triggered approach can achieve improved control performance and lower computational cost in comparison with the existing methods.
Proceedings of the International Joint Conference on Neural Networks
Dong, Lu, Xiangnan Zhong, Changyin Sun, and Haibo He. "Predictive event-triggered control based on heuristic dynamic programming for nonlinear continuous-time systems." Proceedings of the International Joint Conference on Neural Networks 2015-September, (2015). doi:10.1109/IJCNN.2015.7280842.