An adaptive feedback linearized model predictive controller design for a nonlinear multi‐input multi‐output system

Abstract

SummaryIn this work, an adaptive feedback linearized model predictive control (AFLMPC) scheme is proposed to compensate system uncertainty for a class of nonlinear multi‐input multi‐output system. Initially, a feedback linearization technique is used to transform the nonlinear dynamics into an exact linear model, thereafter, a model predictive control scheme is designed to obtain the desired tracking performance. A suitable constraint mapping algorithm has been developed to map input constraints to the new virtual input of the proposed control scheme. The proposed control scheme utilizes multiple estimation model and the concept of second‐level adaptation technique Pandey et al. (2014) to handle the parametric uncertainty in real‐time. Hence, the adaptive term in the control scheme is used to counteract the effect of model uncertainties and parameter adaptation errors. The effectiveness of the proposed AFLMPC control algorithm has been verified successfully in simulation as well as the experimental setup of the TRMS model. The unavailable states of the nonlinear system have been estimated using an extended Kalman filter based state observer. The performance of the proposed control algorithm has been compared with other existing nonlinear control techniques in simulation and experimental validation.