access icon openaccess Adaptive control of non-affine MIMO systems with input non-linearity and unmodelled dynamics

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 26/11/2018, Accepted 04/12/2018, Published 21/01/2019

In this study, an adaptive neural network control scheme is proposed for a class of multi-input multi-output (MIMO) non-affine systems with unmodelled dynamics and dead-zone non-linear input. This scheme solves the complexity of computation problem, broadens the variables of unmodelled dynamics and cancels the assumption of the neural network approximation error to be bounded. Using the mean value theorem and Young's inequality, only one adaptive parameter is adjusted for the whole MIMO system. By theoretical analysis, all the signals in the closed-loop systems are proved to be semi-globally uniformly ultimately boundedness. The numerical simulation illustrates the effectiveness of the proposed scheme.

Inspec keywords: MIMO systems; closed loop systems; neurocontrollers; nonlinear control systems; uncertain systems; adaptive control; control system synthesis; Lyapunov methods

Other keywords: multiinput multioutput nonaffine systems; adaptive neural network control scheme; dead-zone nonlinear input; MIMO system; input nonlinearity; nonaffine MIMO systems; adaptive control; adaptive parameter; neural network approximation error; closed-loop systems; unmodelled dynamics

Subjects: Stability in control theory; Multivariable control systems; Self-adjusting control systems; Nonlinear control systems; Neurocontrol

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