Data-driven Guaranteed Cost Control for LPV Systems with Experimental Application to the Quanser Aero

This paper presents a data-driven guaranteed cost controller design for Linear Parameter Varying (LPV) systems, both in continuous- and discrete-time settings. In particular, the paper extends recent results on data-driven LPV control design based on Quadratic Matrix Inequalities (QMIs) and the matrix S-lemma to the case of Guaranteed Cost Control (GCC). Moreover, a simplified version of the discrete-time algorithm is proposed to further enhance its practical applicability. The effectiveness of the proposed schemes is demonstrated through numerical examples, whereas the simplified discrete-time algorithm is experimentally validated on the Quanser Aero, a versatile dual-rotor platform, in regulation and reference tracking tasks, confirming its performance in a real-world scenario.