Paper Info
Title
Model-Based Adaptive Event-Triggered Tracking Control of Discrete-Time Nonlinear Systems Subject to Strict-Feedback Form
Abstract
The consumption of communication resources is an essential issue when control tasks are implemented in a wireless network environment. In order to lessen the network resources, a novel model-based (MB) adaptive event-triggered (ET) tracking control scheme is put forward in this article for strict-feedback discrete-time nonlinear systems. In this article, an event-based adaptive model is constructed by the combination of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> -step-ahead predictor and event-sampled neural networks. Then, the adaptive neural model is used for designing the MB ET controller. Besides, a modified ET condition is constructed without any delay. By combining a decoupled backstepping framework, the reverse Lyapunov stability technology is developed to verify the ultimate boundedness of all closed-loop signals and the convergence of the tracking error. Compared to the zero-order hold method, which keeps transmitted state signals unchanged in the interevent period, the proposed MB ET control scheme can keep the real-time update of state signals transmitted to the controller. It means that the triggering error will be smaller by the MB trigger mechanism, thereby improving the event-based tracking performance and further saving communication resources. Comparisons of simulation results are given to verify the effectiveness of the proposed control scheme.
Year
DOI
Venue
2022
10.1109/TSMC.2021.3098025
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Adaptive tracking control,discrete-time systems,event-triggered (ET) control,model-based (MB) control,neural network (NN),strict-feedback structure
Journal
52
Issue
ISSN
Citations 
7
2168-2216
0
PageRank 
References 
Authors
0.34
37
5
Name
Order
Citations
PageRank
Min Wang17627.77
Fenghua Ou200.34
Haotian Shi300.34
Chenguang Yang42213138.71
Xiaoping Liu593384.29