Abstract

Recently, data-driven system identification using sparse regression with L1 regularisation solved the problem to identify simultaneously functional structure and the related parameter estimates. Based on this open-loop framework, the objectives of this paper are to theoretically examine the performance of the data-driven nonlinear system identification using a sequential threshold least-squares (STLSQ) algorithm based on sparse regression with L1 regularisation for closed-loop processes.