Model-Free Adaptive Control for Unknown MIMO Nonaffine Nonlinear Discrete-Time Systems With Experimental Validation

被引：70

作者：

Xiong, Shuangshuang ^{[1
]}

Hou, Zhongsheng ^{[2
]}

机构：

[1] Beijing Informat Sci & Technol Univ, Sch Automat, Beijing 100192, Peoples R China

[2] Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2022年 / 33卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Data models; MIMO communication; Field-flow fractionation; Nonlinear dynamical systems; Heuristic algorithms; Adaptation models; Adaptive control; Data-driven control; full form dynamic linearization (DL); model-free adaptive control (MFAC); multi-input multi-output (MIMO) nonlinear systems; stability;

D O I：

10.1109/TNNLS.2020.3043711

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 article, a model-free adaptive control (MFAC) algorithm based on full form dynamic linearization (FFDL) data model is presented for a class of unknown multi-input multi-output (MIMO) nonaffine nonlinear discrete-time learning systems. A virtual equivalent data model in the input-output sense to the considered plant is established first by using the FFDL technology. Then, using the obtained data model, a data-driven MFAC algorithm is designed merely using the inputs and outputs data of the closed-loop learning system. The theoretical analysis of the monotonic convergence of the tracking error dynamics, the bounded-input bounded-output (BIBO) stability, and the internal stability of the closed-loop learning system is rigorously proved by the contraction mapping principle. The effectiveness of the proposed control algorithm is verified by a simulation and a quad-rotor aircraft experimental system.

引用

页码：1727 / 1739

页数：13

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