Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique

Abstract

Purpose

The proposed Sliding Mode Control-Global Regressive Neural Network (SMC-GRNN) algorithm is an integration of Global Regressive Neural Network (GRNN) and Sliding Mode Control (SMC). Through this integration, a novel structure of GRNN is designed to enable online and. This structure is then combined with SMC to develop a stable adaptive controller for a class of nonlinear multivariable uncertain dynamic systems.

Design/methodology/approach

In this study, a new hybrid (SMC-GRNN) control method is innovatively developed.

Findings

A novel structure of GRNN is designed that can be learned online and then be integrated with the SMC to develop a stable adaptive controller for a class of nonlinear uncertain systems. Furthermore, Lyapunov stability theory is utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system. Eventually, two different numerical benchmark tests are employed to demonstrate the performance of the proposed controller.

Originality/value

A novel structure of GRNN is originally designed that can be learned online and then be integrated with the sliding mode SMC control to develop a stable adaptive controller for a class of nonlinear uncertain systems. Moreover, Lyapunov stability theory is innovatively utilized to ensure the hidden-output weighting values of SMC-GRNN adaptively updated in order to guarantee the stability of the closed-loop dynamic system.

Citation

Anh, H.P.H. and Dat, N.T. (2023), "Robust adaptive control of nonlinear dynamic systems using hybrid sliding mode regressive neural learning technique", Engineering Computations, Vol. 40 No. 3, pp. 657-678. https://doi.org/10.1108/EC-06-2022-0399