Tensor networks for MIMO LPV system identification
Bilal Gunes (TU Delft - Mechanical Engineering)
Jan Willem van Wingerden (TU Delft - Mechanical Engineering)
Michel Verhaegen (TU Delft - Mechanical Engineering)
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Abstract
In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the ‘curse-of-dimensionality’ as they inherit the properties of tensor trains. The proposed algorithm is ‘curse-of-dimensionality’-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods.
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