Title: Adaptive neural network control for a class of MIMO non-affine uncertain systems with input dead-zone nonlinearity and external disturbance

Authors: Nassira Zerari; Mohamed Chemachema; Najib Essounbouli

Addresses: Signal Processing Laboratory, Department Electronics, Faculty of Technology, Constantine 1 University, Constantine 2500, Algeria ' Signal Processing Laboratory, Department Electronics, Faculty of Technology, Constantine 1 University, Constantine 2500, Algeria ' CReSTIC Laboratory, University of Reims Champagne-Ardennes, France

Abstract: This paper studies an adaptive tracking control for a class of multi-input multi-output (MIMO) non-affine nonlinear systems, with input dead-zone nonlinearity and external disturbances. By using the mean-value theorem, the system model is transformed into an affine form so as the difficulty in controlling non-affine systems is overcome. In the proposed control design, neural networks (NNs) are used to approximate the unknown nonlinearities based on their universal approximation properties. To compensate for approximation errors and external disturbances, an adaptive robust control term is introduced. In comparison with existing approaches, the structure of the designed controller is considerably simpler, and can handle a wider range of nonlinear systems. The stability of the closed-loop system is investigated by using Lyapunov theory. The simulation results illustrate the proposed method.

Keywords: adaptive control system; neural networks; non-affine systems; multi-input multi-output; MIMO; dead-zone input.

DOI: 10.1504/IJSCC.2020.106567

International Journal of Systems, Control and Communications, 2020 Vol.11 No.2, pp.142 - 160

Received: 28 Nov 2018
Accepted: 06 May 2019

Published online: 18 Mar 2020 *

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