Finite horizon robust model predictive control with terminal cost constraints
نویسندگان
چکیده
In this paper, we develop a finite horizon model predictive control algorithm which is robust to modelling uncertainties. A moving average system matrix is constructed to capture modelling uncertainties and facilitate the future output prediction. The paper is mainly focused on the step tracking problem. Using linear matrix inequality techniques, the design is converted into a semi-definite optimization problem. Closed-loop stability, known to be one of the most challenging topics in finite horizon model predictive control, is treated by adding extra terminal cost constraints in the semi-definite optimization. A simulation example demonstrates that the approach can be useful for practical applications.
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