Adaptive Multi-Dimensional Taylor Network Tracking Control for a Class of Stochastic Nonlinear Systems With Unknown Input Dead-Zone

In this paper, a multi-dimensional Taylor network (MTN) tracking control scheme is proposed for a class of stochastic nonlinear systems with unknown input dead-zone. The MTNs are used to approximate the nonlinearities, and then, an adaptive MTN controller is constructed via a backstepping technique....

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.34543-34554
Main Authors: Han, Yuqun, Zhu, Shanliang, Yang, Shuguo
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive systems
Artificial neural networks
Backstepping
backstepping technique
Control systems
Control systems design
Controllers
dead-zone
Feedback control
multi-dimensional Taylor network
Nonlinear systems
Stability analysis
stochastic nonlinear systems
Stochastic processes
Stochastic systems
Tracking control
Tracking errors
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Summary:In this paper, a multi-dimensional Taylor network (MTN) tracking control scheme is proposed for a class of stochastic nonlinear systems with unknown input dead-zone. The MTNs are used to approximate the nonlinearities, and then, an adaptive MTN controller is constructed via a backstepping technique. It is proved that the design MTN controller ensures that all signals of the closed-loop system remain bounded in probability, and the tracking error eventually converges to an arbitrarily small neighborhood around the origin in the sense of a mean quartic value. Finally, two numerical examples and one practical example are given to demonstrate the effectiveness of the proposed design method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2849511