State-feedback receding horizon control for constrained uncertain sampled-data systems

This paper investigates the problem of receding horizon controller design for sampled-data systems with parameter uncertainties. Attention is focused on the design of predictive controller which guarantees the asymptotical stability of the closed-loop sampled-data system and reduces the quadratic performance index for all admissible parameter uncertainties. Sufficient condition for the solvability of the problem is in terms of linear matrix inequalities (LMIs). The desired predictive controller can be constructed by solving certain LMIs. An illustrative example is given to demonstrate the effectiveness of the proposed method.