Paper Info
Title
Event-Triggered ADP Control of A Class of Non-Affine Continuous-Time Nonlinear Systems Using Output Information
Abstract
An event-triggered adaptive dynamic programming (ADP) approach is proposed for a class of non-affine continuous-time nonlinear systems with unknown internal states. A neural networks (NNs)-based observer is designed to reconstruct internal states of the system using output information, and then, by the estimation signals, an output feedback ADP control approach is developed in an event-triggered manner. The proposed approach samples the states and updates the control signal only when the triggered condition is violated, and critic NNs are designed to approximate the performance index. Compared with the traditional ADP one under a fixed sampling mechanism, the event-triggered control approach reduces the computation resource and transmission load in the learning process. The stability analysis of the closed-loop system is provided based on the Lyapunov’s theorem. Two simulation results also verify the theoretical claims.
Year
DOI
Venue
2020
10.1016/j.neucom.2019.08.097
Neurocomputing
Keywords
Field
DocType
Event-triggered approach,Observer,Adaptive dynamic programming,Non-affine system,Neural network
Affine transformation,Dynamic programming,Lyapunov function,Nonlinear system,Pattern recognition,Control theory,Artificial intelligence,Sampling (statistics),Observer (quantum physics),Artificial neural network,Mathematics,Computation
Journal
Volume
ISSN
Citations 
378
0925-2312
2
PageRank 
References 
Authors
0.36
0
6
Name
Order
Citations
PageRank
Yang Yang1122.50
Chuang Xu2163.98
Dong Yue33320214.77
Xiangnan Zhong434616.35
Xuefeng Si520.36
Jie Tan6162.54