A Norm-Bounded based MPC strategy for uncertain systems under partial state availability
For control engineers dealing with uncertain systems and partial state feedback, this work provides a computationally tractable MPC strategy that handles input rate constraints, though it is an incremental extension of existing robust MPC methods.
The paper develops a robust MPC scheme for norm-bounded uncertain discrete-time linear systems with partial state measurements, using off-line linear memoryless controller design and on-line predictive control via semi-definite programming. The approach formally addresses input rate constraints.
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.