Sampled-data robust feedback linearization using estimator

Abstract

In this paper, robust control schemes are presented to achieve sampled-data output feedback tracking, for the cases of unknown and known nonlinear minimum phase second order plant (system) models. For known system model case, system output tracks reference trajectory using Extended Kalman Filter (EKF), Unscented Kalman filter (UKF), and Cubature Kalman Filter (CKF). Whereas, for unknown system model case; EKF, UKF and CKF cannot be utilized. For this case, in this paper; State-Space Recursive Least Squares (SSRLS) and Sliding Mode observer (SMO) are employed. SSRLS uses constant velocity model, whereas, SMO requires information about input function only, to track the reference signal. Emulation Design based discrete feedback linearization controller utilizes estimated states to generate control input for plant. The robustness of these sampled-data output feedback control schemes (using estimators) against disturbance and parameter perturbation is demonstrated. It is presented via simulations for magnetic levitation system, that robust tracking is achieved on using estimators (Kalman filters and SMO) in sampled-data output feedback configuration as compared to performing tracking using sampled-data state feedback scheme. Simulation results show that SMO based output feedback tracking is most robust, followed by CKF and EKF based output feedback scheme. UKF based output feedback scheme is robust against external disturbance force, but for case of system parameter perturbation, UKF tracking error takes longer time to converge. SSRLS based scheme behaves poorly in presence of external disturbance force, as SSRLS estimation is based on constant velocity model and not on actual nonlinear system model.