期刊
NEUROCOMPUTING
卷 272, 期 -, 页码 356-364出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.07.008
关键词
Data-driven control (DDC); Event-triggered control (ETC); Model-free adaptive control (MFAC); Radial basis function neural networks (RBFNNs)
资金
- National Natural Science Foundation of China [61621004, 61273148, 61420106016]
- Fundamental Research Funds for the Central Universities [N130604005]
- Research Fund of State Key Laboratory of Synthetical Automation for Process Industries [2013ZCX01]
This paper is concerned with the event-triggered data-driven control problem for nonlinear discrete-time systems. An event-based data-driven model-free adaptive controller design algorithm together with constructing an adaptive event-trigger condition is developed. Different from the existing data-driven model-free adaptive control approach, an aperiodic neural network weight update law is introduced to estimate the controller parameters, and the event-trigger mechanism is activated only if the event-trigger error exceeds the threshold. Furthermore, by combining the equivalent-dynamic-linearization technique with the Lyapunov method, it is proved that both the closed-loop control system and the weight estimation error are ultimately bounded. Finally, two simulation examples are provided to demonstrate the effectiveness of the derived method. (C) 2017 Elsevier B.V. All rights reserved.
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