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.

Attitude stabilization of spacecraft simulator based on modified constrained feedback linearization model predictive control

“…Considering the discrete state space model (25) and expanding the control input 𝝊 MPC over the whole control horizon (N c ), the vector of decision variables can be defined as…”

“…In [23] the combination of MPC and FL is applied to the hydrogen excess ratio regulation. The scholars in [25] proposed an adaptive feedback linearized model predictive control scheme for a class of nonlinear multi‐input multi‐output system. The authors of [24] and [22] develop a MPC‐based controllers for feedback linearized dynamics of life‐support spacecraft and eddy current de‐tumbling of space tumbling targets, respectively.…”

Robust fuzzy model predictive control for nonlinear discrete‐time systems

“…Model Predictive Control (MPC) as a sort of optimization control approach has attracted numerous attention in the industry. 1,2 Commonly, an MPC anticipates the near future of the system under consideration and optimizes a finite horizon cost function regarding specific constraints, while ensuring stability and recursive feasibility. An optimal control sequence is achieved by adopting recently existing information on the system but only the first control law is implemented.…”

Novel Strategy of Adaptive Predictive Control Based on a MIMO-ARX Model

“…The presence of this coupling typically limits the performance of the control tools developed for SISO processes, because these controllers do not take this coupling into account. Therefore, the need for a performance improvement in the field of MIMO processes control has motivated the research of control techniques specifically developed for multivariable processes including linear and nonlinear systems, different control approaches like fuzzy controllers, neural network strategies, MPC and adaptive predictive controllers [14][15][16][17][18].…”