Parameter Estimator based Feedback Linearization Control strategy of Magnetic Levitation System

Magnetic Levitation System (MLS) feedback linearization uses a suitable control law to linearize its input-output dynamics, enabling multiple linear control strategies to be utilized to track the output trajectories to the desired one. The exact knowledge of the MLS parameters like resistance, inductance, mass, and force constant is required for accurate closed-loop control. In this paper, a Dynamic Regressor Estimation and Mixing (DREM) estimator is proposed which evaluates an entire closed-loop structure without considering any prior knowledge of the MLS parameters. Because of finite-time convergence and enhanced transient responsiveness, DREM is used to estimate the underlying MLS parameters, and these parameter estimates are updated in the transformed system and control law. A systematic approach for feedback linear control of MLS with DREM is presented in this paper. The results of simulations on the MLS model illustrates the effectiveness of the proposed approach.