An integral architecture for identification of continuous-time state-space LPV models⁎This work was partially supported by the European H2020-CS2 project ADMITTED, Grant agreement no. GA832003. Issue 8 (2021) - Mejari, Manas; Mavkov, Bojan; Forgione, Marco; Piga, Dario
Abstract: This paper presents an integral architecture for direct identification of continuous-time linear parameter-varying (LPV) state-space models. The main building block of the proposed architecture consist of an LPV model followed by an integral block, which is used to approximate the continuous-time state map of an LPV representation. The unknown LPV model matrices are estimated along with the state sequence by minimizing a properly constructed dual-objective criterion. A coordinate descent algorithm is employed to optimize the desired objective, which alternates between computing the unknown LPV matrices and estimating the state sequence. Thanks to the linear parametric structure induced by the LPV models, the unknown parameters within each coordinate descent step can be computed analytically via ordinary least squares. The effectiveness of the proposed methodology is assessed via a numerical example.