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arXiv · 2401.10052

Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification

Johan Kon·Jeroen van de Wijdeven·Dennis Bruijnen·Roland Tóth·Marcel Heertjes·Tom Oomen
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Abstract

Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e.g., through solving Linear Matrix Inequalities. In this paper, an unconstrained DT-LPV-IO parameterization is developed which gives a stable model for any choice of model parameters. To achieve this, it is shown that all quadratically stable DT-LPV-IO models can be generated by a mapping of transformed coefficient functions that are constrained to the unit ball, i.e., a small-gain condition. The unit ball is then reparameterized through a Cayley transformation, resulting in an unconstrained parameterization of all quadratically stable DT-LPV-IO models. As a special case, an unconstrained parameterization of all stable DT linear time-invariant transfer functions is obtained. Identification using the stable DT-LPV-IO model with neural network coefficient functions is demonstrated on a simulation example of a position-varying mass-damper-spring system.

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Johan Kon, Jeroen van de Wijdeven, Dennis Bruijnen, Roland Tóth, Marcel Heertjes, Tom Oomen. 2024-01-18. Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification. https://arxiv.org/abs/2401.10052

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