Robust Model Predictive Control Design

Abstract

Recently, many control designers have worked on design methods that meet several design specifications called multi-objective control design. However, the main challenge for the Model Predictive Control design is the high computational load preventing its application to the fast dynamic control of the system in real time. To meet this challenge, this paper proposes a new modified Model Predictive Control design for nonlinear systems with probabilistic uncertainties that guarantees robust stability and performance of the systems, using the Linear Matrix Inequality"LMI". Introducing our robust Model Predictive Control state feedback, the control law will be calculated by step-by-step optimization, and the LMI solutions can be found to stabilize the Linear Parameter-Varying "LPV"system with disturbance rejection ability. Then, a Tensor Product (TP) model transformation is constructed as a powerful tool in the modeling of the complex nonlinear systems.

Keywords

• robustness
• model predictive
• multi-objective optimization
• control algorithms
• lpv
• lmi
• tensor product