A Norm-Bounded based MPC strategy for uncertain systems under partial state availability

A robust model predictive control scheme for a class of constrained norm-bounded uncertain discrete-time linear systems is developed under the hypothesis that only partial state measurements are available for feedback. Off-line calculations are devoted to determining an admissible, though not optimal, linear memoryless controller capable to formally address the input rate constraint; then, during the on-line operations, predictive capabilities complement the off-line controller by means of N steps free control actions in a receding horizon fashion. These additive control actions are obtained by solving semi-definite programming problems subject to linear matrix inequalities constraints.
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