期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 31, 期 6, 页码 1968-1981出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2927595
关键词
Adaptive systems; Artificial neural networks; Actuators; Stochastic processes; Nonlinear dynamical systems; Adaptive neural control; discrete-time strict-feedback systems; event-based control; stochastic noise; triggering threshold compensation
类别
资金
- National Natural Science Foundation of China [61773169, 61890922, 61873148, 61873082, U1509205]
- Guangdong Provincial Natural Science Foundation of China [2017A030313381, 2017A030313369]
- Guangzhou Science and Technology Project [201904010295]
- Zhejiang Provincial Natural Science Foundation of China [LR16F030003]
- Research Fund for the Taishan Scholar Project of Shandong Province of China
- Fundamental Research Funds for the Central Universities
- Royal Society of the U.K.
- National Postdoctoral Program for Innovative Talents in China [BX20180202]
- Alexander von Humboldt Foundation of Germany
This paper investigates the event-triggered (ET) tracking control problem for a class of discrete-time strict-feedback nonlinear systems subject to both stochastic noises and limited controller-to-actuator communication capacities. The ET mechanism with fixed triggering threshold is designed to decide whether the current control signal should be transmitted to the actuator. A systematic framework is developed to construct a novel adaptive neural controller by directly applying the backstepping procedure to the underlying system. The proposed framework overcomes the noncausality problem, avoids the possible controller-related singularity problem, and gets rid of the neural approximation of the virtual control laws. Under the ET mechanism, the corresponding ET-based actuator is put forward by introducing an ET threshold compensation operator. Such a compensation operator (with an adjustable design parameter) is subtly designed based on a hyperbolic tangent function and a sign function. The threshold compensation error is analytically characterized in terms of a time-varying parameter, and the error bound is shown to be relatively small that is dependent on the adjustable design parameter. Compared with the traditional ET-based actuator without the compensation operator, the proposed ET-based actuator exhibits several distinguished features including: 1) improvement of the tracking accuracy (especially at the triggering instants); 2) further mitigation of the communication load; and 3) enlargement of the allowable range of the ET threshold. These features are illustrated by numerical and practical examples.
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