Adaptive Control for Nonaffine Nonlinear Systems Using Reliable Neural Network Approximation

被引：10
|
作者：
Sun, Tairen [1 ]
Pan, Yongping [2 ,3 ]
机构：
[1] Jiangsu Univ, Dept Elect & Informat Engn, Zhenjiang 212013, Peoples R China
[2] Natl Univ Singapore, Dept Biomed Engn, Singapore 117583, Singapore
[3] Natl Univ Singapore Suzhou, Res Inst, Suzhou 215123, Peoples R China
来源：
IEEE ACCESS | 2017年 / 5卷
基金：
中国国家自然科学基金;
关键词：
Neural network; adaptive control; nonaffine nonlinear system; self-structuring approximator; reliable approximation; ROBUST-CONTROL; MOBILE ROBOTS; DESIGN;
D O I：
10.1109/ACCESS.2017.2763628
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a once-differentiable control strategy for a class of uncertain nonaffine nonlinear systems based on self-structuring neural networks (SSNNs) approximation, such that the system output tracks the desired trajectory. The optimal weight for each neuron in current SSNN is time-varying signals factually, and current stability analysis is only fit for a dwell time. Current SSNN control laws are not smooth and even not continuous, due to addition or pruning of neurons in the approximation procedure. In this paper, a new SSNN estimator and a new weight update law are proposed to ensure the optimal SSNN weights being constant values and the control law being once-differentiable. The effectiveness of the proposed control law is illustrated by the stability analysis in the whole tracking procedure and shown by the simulation results.
引用
收藏
页码：23657 / 23662
页数：6
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