Data-Driven Dissipativity Analysis of Linear Parameter-Varying Systems

2024/06/21 by Chris Verhoek, Julian Berberich, Sofie Haesaert +2 · 3 citations
Physics and Astronomy · Engineering · #Model Reduction and Neural Networks #Fault Detection and Control Systems #Hydraulic and Pneumatic Systems

paper · doi:10.1109/tac.2024.3417855

Abstract

In this article, we derive direct data-driven dissipativity analysis methods for linear parameter-varying (LPV) systems using a single sequence of input-scheduling-output data. By means of constructing a semidefinite program subject to linear matrix inequality constraints based on this <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data-dictionary</i>, direct data-driven verification of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">(Q,S,R)</tex-math></inline-formula>-type of dissipativity properties of the data-generating LPV system is achieved. Multiple implementation methods are proposed to achieve efficient computational properties and to even exploit structural information on the scheduling, e.g., rate bounds. The effectiveness and tradeoffs of the proposed methodologies are shown in simulation studies of academic and physically realistic examples.

Citations

• Data-driven simulation and control
• Dissipative dynamical systems part I: General theory
• Data-Driven Model Predictive Control With Stability and Robustness Guarantees
• Data-Driven Inference on Optimal Input–Output Properties of Polynomial Systems With Focus on Nonlinearity Measures

Cited by

• An Input-Output Data-Driven Dissipativity Approach for Compositional Stability Certification of Interconnected LTI MIMO Systems
• Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
• Direct data-driven interpolation and approximation of linear parameter-varying system trajectories

Related

• Data-driven dissipativity analysis: application of the matrix S-lemma
• Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
• A behavioral approach for LPV data-driven representations
• Synthesis of Dissipative Systems Using Input-State Data
• Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control