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

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.

► We propose a robust hybrid control scheme for nonlinear MIMO systems.
► The hybrid approach is well suited for effective control of nonlinear MIMO systems.
► The scheme uses variable structure and nonlinear inversion controllers.
► Neural network compensation is used to handle system uncertainties.