Nonlinear disturbance observer based multiple-model adaptive explicit model predictive control for nonlinear MIMO system

In this article, a nonlinear disturbance observer-based multiple-model adaptive explicit model predictive control (NDO-MMAEMPC) scheme is developed for a nonlinear MIMO system with parametric uncertainty as well as an external disturbance. The proposed method manages external disturbances without affecting the degree of smoothness of the control signals. Further, to cope with the unknown system uncertainty, blending-based adaptive identification schemes are used for the same class of systems. For each identification model, an explicit nonlinear model predictive controller is computed off-line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of explicit controllers are being blended online using adaptive weight. An extended Kalman filter algorithm is employed for the estimation of the unavailable states of the nonlinear system. Using an aerodynamic laboratory setup, the effectiveness of the proposed control algorithm is verified.

Automated summary

In this article, a nonlinear disturbance observer-based multiple-model adaptive explicit model predictive control (NDO-MMAEMPC) scheme is proposed for a nonlinear MIMO system with parametric uncertainty and external disturbance. The proposed method effectively manages external disturbances without compromising control signal smoothness. Adaptive identification schemes based on blending are used to handle unknown system uncertainty. Offline computation of explicit nonlinear model predictive controllers is performed in advance for each identification model, reducing the computational burden during operation. The control inputs generated by the set of explicit controllers are blended online using adaptive weight. An extended Kalman filter algorithm is employed for estimating the unavailable states of the nonlinear system. The effectiveness of the proposed control algorithm is demonstrated through an aerodynamic laboratory setup.