Nonlinear disturbance observer‐based adaptive feedback linearized model predictive controller design for a class of nonlinear systems

Abstract

Adaptive control with less prior information of a nonlinear system subjected to external disturbance and parametric uncertainties is a longstanding problem in the control community. In this paper, a nonlinear disturbance observer‐based adaptive feedback linearized model predictive control (NDO‐AFLMPC) technique is proposed for a class of nonlinear MIMO systems. In this approach, a nonlinear disturbance observer (NDO) is used to observe the external disturbance of the system. A suitable constraint mapping algorithm is developed to map the input–output constraints within the proposed control scheme. Here, a multiple estimation model and the concept of second‐level adaptation technique is used to handle the parametric uncertainty of the real‐time plant. The boundedness of the estimated parameter is solved by developing the projection‐based parameter adaptation laws. Using an aerodynamic laboratory set‐up, known as the twin‐rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm is verified. The complete state information of the real‐time system is provided to the proposed adaptive controller from an extended Kalman filter (EKF)‐based state observer. The performance of the proposed control algorithm is compared with other existing nonlinear control algorithms in the presence of unknown external disturbance and parametric uncertainties.