Finite-Time Parameter Estimation of Separable Nonlinearly Parameterized Regressions With Application

Abstract

This letter addresses the finite-time parameter estimation problem for separable nonlinearly parameterized regression equations (NLPREs). The nonlinear vector function of unknown parameters in the considered NLPRE is such that, after a coordinate change, some components satisfy the so-called P-monotonicity condition. By utilizing the dynamic regressor extension and mixing (DREM) technique, a set of new scalar NLPREs is obtained, and based on which the finite-time parameter estimator is designed. The finite-time convergence of the parameter estimation error vector is rigorously proved under the interval excitation (IE) condition. The proposed method is further applied to the parameter estimation of the windmill power coefficient. Simulation results demonstrate the validity and advantages of the proposed method.