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

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans Pub Date : 2021-04-01 DOI:10.1109/TSMC.2019.2915950

Ahmad Jobran Al-Mahasneh, S. Anavatti, M. Garratt, Mahardhika Pratama

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引用次数: 16

Abstract

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.

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一类非线性时变系统基于广义回归神经网络和滑模控制的稳定自适应控制器

寻找各种控制和估计方法之间的协同作用，可以有效地、系统地控制非线性动态系统。本文提出了一种由广义回归神经网络(grnn)和滑模控制(SMC)组成的非线性多输入多输出(MIMO)动态系统控制器设计。该设计将GRNN从离线回归模型转变为在线自适应控制器。建议的控制器不需要任何预训练，并且从头开始快速学习。它采用了计算复杂度低的算法，提供了准确稳定的性能。通过一个通用MIMO非线性动态系统和一个变重心六旋翼机模型验证了该控制器的性能。结果与标准PID控制器进行了比较。此外，利用Lyapunov稳定性方法验证了GRNNSMC控制器的稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Systems Man and Cybernetics Part A-Systems and Humans 工程技术-计算机：控制论

自引率

0.00%

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审稿时长

6.0 months

期刊介绍： The scope of the IEEE Transactions on Systems, Man, and Cybernetics: Systems includes the fields of systems engineering. It includes issue formulation, analysis and modeling, decision making, and issue interpretation for any of the systems engineering lifecycle phases associated with the definition, development, and deployment of large systems. In addition, it includes systems management, systems engineering processes, and a variety of systems engineering methods such as optimization, modeling and simulation.