Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems

被引：472

作者：

Hou, Zhongsheng ^{[1
]}

Jin, Shangtai ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Adv Control Syst Lab, Sch Elect & Informat Engn, Beijing 100044, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2011年 / 22卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Compact form dynamic linearization; data-driven control; model-free adaptive control; multiple-input and multiple-output nonlinear system; partial form dynamic linearization; pseudo-partial derivative; stability; CONTROL DESIGN; OPTIMIZATION;

D O I：

10.1109/TNN.2011.2176141

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed based on a new dynamic linearization technique (DLT) with a novel concept called pseudo-partial derivative for a class of general multiple-input and multiple-output nonlinear discrete-time systems. The DLT includes compact form dynamic linearization, partial form dynamic linearization, and full form dynamic linearization. The main feature of the approach is that the controller design depends only on the measured input/output data of the controlled plant. Analysis and extensive simulations have shown that MFAC guarantees the bounded-input bounded-output stability and the tracking error convergence.

引用

页码：2173 / 2188

页数：16

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