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

Yuqun Han | Zendy; Shanliang Zhu | Zendy; Shuguo Yang | Zendy

Open Access

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

Author(s) -

Yuqun Han,

Shanliang Zhu,

Shuguo Yang

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2849511

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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