Data-Driven Disturbance Rejection Design with Stability Guarantees for Scheduling Parameter Variations

This paper presents a novel data-driven method for the synthesis of linear parameter-varying (LPV) controllers aimed at adaptive disturbance rejection. The approach leverages frequency-domain input/output response data from a linear time-invariant (LTI) multiple-input multiple-output (MIMO) system, eliminating the need for a parametric model. The designed LPV controller guarantees system stability even under arbitrarily fast variations of the scheduling parameter corresponding to the estimated harmonic disturbance frequencies. Control design is carried out in the frequency domain using performance constraints at selected operating points representing stationary disturbance frequencies. Then, Integral Quadratic Constraints (IQC) are employed to analyse the closed-loop stability under scheduling parameter variations. The IQC-based algorithm also determines the admissible range of the scheduling parameter and can incorporate upper bounds on its variation rate to reflect physical system limitations. The method is experimentally validated on a hybrid microvibration damping (MIVIDA) platform for space applications. An LPV controller is designed and implemented to reject unknown timevarying harmonic disturbances. Experimental results demonstrate the effectiveness of the approach in achieving robust disturbance rejection and closed-loop stability.