Based On Lmi Robust Predictive Control

Almost all practical systems are subject to some constrains. Predictive control, which is the most popular control strategy for constrained systems, has ever-increasing wide application in industry. These results from its notable capability of handling constraints and optimization over some performance index in a systematic way during both the controller design and implementation stage. On the other hand, the model, which is used to describe the dynamics of controlled system, always has some uncertainty. In order to guarantee the specified performance, model uncertainty must be taken into account properly. Robust model predictive control is referred to the predictive control method which can make the performance index within the acceptable range in the presence of model uncertainty.Based on the existing theoretical results on model predictive control, this thesis is devoted to the development of the framework of robust model predictive control with guaranteed robust feasibility, robust stability and real-time applicability. To achieve this goal, the relevant theory and approaches, such as linear matrix inequalities (LMI), Lyapunov functional method, are employed in the research work. Specifically, the main contribution of this thesis includes:(1) This thesis analyzes the three fundamental problems about model predictive control, classifies the methods for robust model predictive control, summarizes the latest research progress on the existing theoretical results.(2) The robust predictive control for uncertain discrete-time systems with time-delay and actuator saturation is discussed, using the state feedback and Lyapunov-Krasovskii. The control law is obtained by solving a linear matrix inequality (LMI) optimization problem on-line. The feasibility of optimization problem at the original time implies robust stability of the closed-loop system.(3) The problem of robust predictive control for more common uncertain system—linear fractional transformation (LFT) is studied. The sufficient conditions which keep the system stabilization are gained. Then the numerical indicates the validity of the results.