Robust Model Predictive Control For Singular Systems

Predictive control, which is known as a new type of control approach, has been applied successful in industry. Because of the complexity of the system and various factors in industry, the model used to describe the dynamics of controlled system always has some uncertainty. 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. However, the main work is concerned to general systems. Singular systems exist in various fields, and it is very important to study the problem of robust predictive control for singular systems.Based on both the study methods of general systems and the particularity of singular systems, this paper studies the problem of robust predictive control for singular systems. The main contents are stated as follows:1. The method of robust state feedback model predictive control is presented for linear singular systems with polytopic uncertainties and norm-bounded parameter uncertainties. The state feedback control law can be obtained by solving linear matrix inequalities and the robust stability of the closed systems is guaranteed by the initial feasible solutions of the optimization problem.2. Combining the robust concept of H∞control and the moving horizon principle of model predictive control, a robust predictive algorithm based on H∞theory is presented. By LMI, the control law is obtained and the closed system is asymptotically stable.3. A robust predictive algorithm for parameter uncertain singular systems with state delay is presented. Receding horizon implementation of feasible solutions guarantees closed-loop stability.4. An off-line robust predictive control algorithm for linear singular systems with polytopic uncertainties. The control law and corresponding ellipsoid are off-line determined. This off-line algorithm can reduce the computation and guarantee closed-loop stability.