TY - GEN
T1 - Predictive event-triggered control based on heuristic dynamic programming for nonlinear continuous-time systems
AU - Dong, Lu
AU - Zhong, Xiangnan
AU - Sun, Changyin
AU - He, Haibo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - 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.
AB - 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.
KW - Computational modeling
UR - https://www.scopus.com/pages/publications/84951061522
U2 - 10.1109/IJCNN.2015.7280842
DO - 10.1109/IJCNN.2015.7280842
M3 - 会议稿件
AN - SCOPUS:84951061522
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Joint Conference on Neural Networks, IJCNN 2015
Y2 - 12 July 2015 through 17 July 2015
ER -