Title | ||
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Predictive Event-Triggered Control Based On Heuristic Dynamic Programming For Nonlinear Continuous-Time Systems |
Abstract | ||
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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. |
Year | Venue | Field |
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2015 | 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | Dynamic programming,State vector,Optimal control,Nonlinear system,Performance index,Control theory,Computer science,Model predictive control,Event triggered,Heuristic dynamic programming |
DocType | ISSN | Citations |
Conference | 2161-4393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Lu Dong | 1 | 0 | 0.68 |
Xiangnan Zhong | 2 | 346 | 16.35 |
Changyin Sun | 3 | 2002 | 157.17 |
Haibo He | 4 | 3653 | 213.96 |