Learning Stable and Robust Linear Parameter-Varying State-Space Models

• URL: http://arxiv.org/abs/2304.01828v2
• Date: Tue, 26 Sep 2023 15:36:44 GMT
• Title: Learning Stable and Robust Linear Parameter-Varying State-Space Models
• Authors: Chris Verhoek and Ruigang Wang and Roland T\'oth
• Abstract summary: This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. Since the parametrizations are direct, the models can be trained using unconstrained optimization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $\gamma$. Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem.

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