Nonlinear inversion-based control with adaptive neural network compensation for uncertain MIMO systems

Authors:
Jinzhu Peng;Rickey Dubay
Affiliations:
Department of Mechanical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada E3B 5A3;Department of Mechanical Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada E3B 5A3
Venue:
Expert Systems with Applications: An International Journal
Year:
2012

Citing 11
Cited 1

Multilayer feedforward networks are universal approximators

Neural Networks
Feedback linearization using neural networks

Automatica (Journal of IFAC)
Adaptive H∞ tracking control for a class of uncertain nonlinear systems using radial-basis-function neural networks

Neurocomputing
Intelligent adaptive control for MIMO uncertain nonlinear systems

Expert Systems with Applications: An International Journal
Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics

Mathematics and Computers in Simulation
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems

Information Sciences: an International Journal
Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stable adaptive control using fuzzy systems and neural networks

IEEE Transactions on Fuzzy Systems
Brief Robust tracking control for nonlinear MIMO systems via fuzzy approaches

Automatica (Journal of IFAC)
RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology

IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control

IEEE Transactions on Neural Networks

An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

A robust output feedback control scheme for uncertain nonlinear multiple-input and multiple-output (MIMO) systems is proposed, which combines a nonlinear inversion-based controller with a neural network-based robust compensator. The nonlinear inversion-based controller acts as the main controller, and a neural network with an adaptive update law is designed to model the unknown system dynamics, a variable structure controller is employed to eliminate the effect of the neural network approximation errors and to ensure the system stability. Furthermore, an H"~ controller which is a component of the robust compensator is designed to achieve a certain robust tracking performance and to attenuate the effect of external disturbances to a prescribed level. The proposed approach indicates that the nonlinear inversion-based control method is also valid for controlling uncertain nonlinear MIMO systems with uncertainties and disturbances, provided that a compensative controller is designed appropriately. Simulation results demonstrated that the proposed controller performed better in comparison to the nonlinear inversion-based control method and an advanced neural network-based hybrid controller.