Consistent and computationally efficient estimation for stochastic LPV state-space models: realization based approach

Manas Mejari, Mihály Petreczky

Abstract: The article presents an identification algorithm for stochastic Linear Parameter-Varying State-Space Affine (LPVSSA) representations, where the dependency of state-space matrices on scheduling signals is affine. Based on stochastic realization theory, a computationally efficient and statistically consistent identification algorithm is proposed to estimate the LPV model matrices, which are computed from the empirical covariance matrices of outputs and scheduling signal observations. The effectiveness of the proposed realization algorithm is shown via a numerical case study.