Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems

被引：16

作者：

Al-Mahasneh, Ahmad Jobran ^{[1
]}

Anavatti, Sreenatha G. ^{[1
]}

Garratt, Matthew A. ^{[1
]}

Pratama, Mahardhika ^{[2
]}

机构：

[1] Univ New South Wales, Sch Engn & Informat Technol, Canberra, ACT 2612, Australia

[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 04期

关键词：

Training; Nonlinear dynamical systems; Control systems; MIMO communication; Neural networks; Uncertainty; Estimation; Adaptive control; generalized regression neural network (GRNN); intelligent control (IC); neuro-sliding mode control; stability;

D O I：

10.1109/TSMC.2019.2915950

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Finding synergy between a variety of control and estimation approaches can lead to effective solutions for controlling nonlinear dynamic systems in an efficient and systematic manner. In this paper, a novel controller design consisting of generalized regression neural networks (GRNNs) and sliding mode control (SMC) is proposed to control nonlinear multi-input and multi-output (MIMO) dynamic systems. The proposed design transforms GRNN from an offline regression model to an online adaptive controller. The suggested controller does not require any pretraining and it learns quickly from scratch. It uses a low computational complexity algorithm to provide accurate and stable performance. The proposed controller (GRNNSMC) performance is verified with a generic MIMO nonlinear dynamic system and a hexacopter model with a variable center of gravity. The results are compared with the standard PID controller. In addition, the stability of the GRNNSMC controller is verified using the Lyapunov stability method.

引用

页码：2525 / 2535

页数：11

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