Feedback Linearization and Model Reference Adaptive Control of a Magnetic Levitation System

Abstract

Abstract:The aim of this paper is to combine two techniques to control a nonlinear Magnetic Levitation System (MLS). Firstly, a feedback linearization technique (here, exact linearization with state feedback) is applied to obtain a linear system. Secondly, the linearization is made via direct cancellation of nonlinear functions, which represent the phenomenological model of the system. Finally, to deal with the presence of uncertainty in the system model, an adaptive controller is used. The controller is based on model reference adaptive control to estimate the functions that contain the nonlinearities of the system. The exact linearization and the adaptive controller were implemented in a simulated environment (Matlab Simulink ©). The linear adaptive controller structure guarantees the parameters adaptation and the overall stability of the system. The results show that the controller output signal tracks a reference input signal with a small error.

Development of a new linearizing controller using Lyapunov stability theory and model reference control

“…The dynamic of the system is derived based on the first principles of basic electrical and mechanical laws. The nonlinear reduced order of the magnetic levitation system described in Figure 2 is selected for the investigation [29]. This reduced order model is defined as:…”

“…To make the system stable, all the poles must be with real negative parts. The control 𝒗 is obtained as (29).…”

Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model

“…Recently, a variety of control strategies have been presented and discussed for the levitation system. For instance, the feedback linearization [3] or feedforward linearization [4] technology was applied to design a trajectory tracking controller for a nonlinear maglev system. Fuzzy-PID control [5] and adaptive control [3,6,7], sliding-model control [8], fuzzy control [9,10], neural network control [11] and predictive control [1,[12][13][14] were also used to achieve the trajectory tracking in order to improve the robustness and to expand the range of effective control.…”

Setpoint Tracking for the Suspension System of Medium-Speed Maglev Trains via Reinforcement Learning

“…Although it outperforms the classical tuned PID controller, the performance may significantly degrade with an increasing deviation from nominal operating points. In [5]- [7], a nonlinear controller is designed by using feedback linearization. Robust control [8], [9] and low-bias control [10] are also utilized for the regulation problem.…”