Continuous time model identification via Poisson moment functional approach

Estimation of parameters forms an important part of the adaptive control. Various methods are elaborated at continuous time and at discrete time on that subject. These methods are formed by filtering process and system identification process. Filtering process supplies considerable advantages in system identification. The first advantage is that it facilitates finding parameters without needing to find discrete time model. And the other advantage is that it is usable for finding out continuous time model used for the methods to identify discrete time models. In this study, Linear İntegral filtering process-rectified by Sagara and Zhao (1989) - and Poisson Moment function filtering process - developed by Sinha and Rao - are applied to the commonly known Least Square system identification and Instrumental Variable methods. Additionally, simulations are achieved by comparing both filtering methods, and system identification algorithms. For setting model system of parameters, a secondary system is carried out and simulation results are analysed. Key words: (System Identification, Poisson Moment function, Linear Integral filter, Least Square, Instrumental Variable)