Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

被引：4

作者：

Wang, Shubo ^{[1
]}

Yu, Haisheng ^{[1
]}

Gao, Xuehui ^{[2
]}

Wang, Na ^{[1
]}

机构：

[1] Qingdao Univ, Coll Automat & Elect Engn, Qingdao 266071, Peoples R China

[2] Shandong Univ Sci & Technol, Dept Mech & Elect Engn, Tai An 2701019, Shandong, Peoples R China

来源：

COMPLEXITY | 2018年

基金：

中国国家自然科学基金;

关键词：

DYNAMIC SURFACE CONTROL; PURE-FEEDBACK SYSTEMS; SLIDING MODE CONTROL; OUTPUT-FEEDBACK; SERVO SYSTEMS; DISTURBANCE OBSERVER; PARAMETER-ESTIMATION; CONSENSUS TRACKING; NEURAL-CONTROL; MOTION CONTROL;

D O I：

10.1155/2018/8925838

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper proposes an adaptive barrier controller for servomechanisms with friction compensation. A modified LuGre model is used to capture friction dynamics of servomechanisms. This model is incorporated into an augmented neural network (NN) to account for the unknown nonlinearities. Moreover, a barrier Lyapunov function (BLF) is utilized to each step in a backstepping design procedure. Then, a novel adaptive control method is well suggested to ensure that the full-state constraints are within the given boundary. The stability of the closed-loop control system is proved using Lyapunov stability theory. Comparative experiments on a turntable servomechanism confirm the effectiveness of the devised control method.

引用

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页数：10

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