Robust Gain-Scheduling Output Feedback Control With Noisy Scheduling Parameters

Abstract

Robust Gain-Scheduling (RGS) control strategy has been considered in this paper. In contrast to the conventional gain-scheduling synthesis methods, the scheduling parameters are assumed to be inexactly measured. This is a practical assumption since measurement noise is inevitable even with very accurate sensors. Multi-simplex modeling approach was used to model the scheduling parameters and their uncertainties in a convex domain. Sufficient conditions in terms of Parametrized Linear Matrix Inequalities (PLMIs) for synthesizing dynamic output-feedback controllers are derived. The resulting controller not only guarantees robust stability and H2 performance but also ensures robustness against scheduling parameters uncertainties. The effectiveness of the developed conditions is demonstrated through numerical example with simulation and comparisons with existing approaches from literature. The comparison results confirm that the developed approach outperforms the existing ones considerably.