System Identification using LMS, RLS, EKF and Neural Network

Model accuracy is the most important step towards efficient control design. Various system identification techniques exist which are used to identify model parameters. However, these techniques have their merits and demerits which need to be considered before selecting a particular system identification technique. In this paper, we compared different types of system identification techniques and used them to identify our DC- motor use-case. Using the identified system, we designed different discrete PI controllers in order to investigate the system response. We concluded EKF provided the best performance in terms of parameter accuracy and convergence rate.