Data-Driven Gain Scheduling Control of Linear Parameter-Varying Systems using Quadratic Matrix Inequalities

Abstract

This paper synthesizes a gain-scheduled controller to stabilize all possible Linear Parameter-Varying (LPV) plants that are consistent with measured input/state data records. Inspired by prior work in data informativity and LTI stabilization, a set of Quadratic Matrix Inequalities is developed to represent the noise set, the class of consistent LPV plants, and the class of stabilizable plants. The bilinearity between unknown plants and `for all' parameters is avoided by vertex enumeration of the parameter set. Effectiveness and computational tractability of this method is demonstrated on example systems.

arXiv comment14 pages, 2 figures

References in corpus (4)

• Data-Driven Model Predictive Control with Stability and Robustness Guarantees
• Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
• Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems
• Quadratic matrix inequalities with applications to data-based control

Cited by in corpus (8)

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• Data-based Transfer Stabilization in Linear Systems
• Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control
• Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
• Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
• Online Data-Driven Adaptive Control for Unknown Linear Time-Varying Systems
• Chebyshev centers and radii for sets induced by quadratic matrix inequalities
• A Linear Parameter-Varying Approach to Data Predictive Control