Continuous time system identification of nonparametric models with constraints

This paper proposes a continuous time systemidentification approach with constraints. It shows thatby incorporating physical parameter information knowna priori as hard constraints, the traditionalparameter estimation schemes are modified to minimize aquadratic cost function with linear inequalityconstraints. Using the structure of Frequency SamplingFilters as the vehicle, the paper shows that theconstraints can be readily imposed on continuous timefrequency response estimation and step responseestimation. In particular, a priori knowledge inboth time-domain and frequency domain is utilizedsimultaneously as the constraints for the optimal parameter solution. A Monte-Carlo simulation study with 100 noise realization is used to demonstrate the improvement of the estimation results.