Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems

Abstract

We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances. A data-set consisting of a single state-input-scheduling trajectory is gathered from the system, which is directly utilized to compute polytopic RCI set and controllers by solving a semidefinite program. The proposed method does not require an intermediate LPV model identification step. Through a numerical example, we show that the proposed approach can generate RCI sets with a relatively small number of data samples when the data satisfies certain excitation conditions.

arXiv comment6 pages, 3 figures. Accepted for publication, IEEE Control System Letters (LCSS) 2023

References in corpus (5)

• Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
• Controller design for robust invariance from noisy data
• Data-Driven Predictive Control for Linear Parameter-Varying Systems
• Data-Driven Gain Scheduling Control of Linear Parameter-Varying Systems using Quadratic Matrix Inequalities
• Synthesizing Safety Controllers for Uncertain Linear Systems: A Direct Data-driven Approach

Cited by in corpus (1)

• Bias correction and instrumental variables for direct data-driven model-reference control