State estimation and simulation of the magnetic levitation system of a high-speed Maglev train

2011 ◽  
Author(s):  
Guangwei Shu ◽  
Reinhold Meisinger
Keyword(s):  
State Estimation ◽  
High Speed ◽  
Magnetic Levitation ◽  
Maglev Train ◽  
Levitation System ◽  
Magnetic Levitation System

scholarly journals LQR state feedback controller with precompensator for magnetic levitation system

2021 ◽  
Vol 2111 (1) ◽  
pp. 012004
Author(s):  
A Winursito ◽  
G N P Pratama
Keyword(s):  
Steady State ◽  
High Speed ◽  
State Feedback ◽  
Magnetic Levitation ◽  
Feedback Controller ◽  
Steel Ball ◽  
State Feedback Controller ◽  
Wide Range ◽  
Levitation System ◽  
Magnetic Levitation System

Abstract Magnetic levitation system (MLS) is a nonlinear system that attracts the attention of many researchers, especially control engineers. It has wide range of application such as robotics, high-speed transportation, and many more. Unfortunately, it is not a simple task to control it. Here, we utilize state feedback controller with Linear-Quadratic Regulator (LQR) to regulate the position of a steel-ball in MLS. In addition, we also introduce the precompensator to nullify the steady-state errors. The linearized model, controller, and precompensator are simulated using Matlab. The results and simulation verify that the state feedback controller and precompensator succeed to stabilize the position of steel-ball at the equilibrium for 0.1766 seconds and no steady-state errors.

scholarly journals Model predictive control of a magnetic levitation system using two-level state feedback

2020 ◽  
Vol 53 (5-6) ◽  
pp. 962-970
Author(s):  
Zhenlin Zhang ◽  
Yonghua Zhou ◽  
Xin Tao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
State Feedback ◽  
Magnetic Levitation ◽  
Air Gap ◽  
State Variables ◽  
Maglev Train ◽  
Levitation System ◽  
Magnetic Levitation System

The magnetic levitation system is a critical part to guarantee safe and reliable operations of a maglev train. In this paper, the control strategy is proposed for the magnetic levitation system based on the model predictive control incorporating two-level state feedback. Taking advantage of the measurable state variables, that is, air gap, electromagnet acceleration, and control current through high-resolution sensor measurement, the first-level nonlinear state feedback is to linearize the unstable nonlinear magnetic levitation system, and the second-level linear state feedback is to further stabilize the system and improve the dynamic performances, which together provide a stable prediction model. The simulation results demonstrate that the proposed control strategy can ensure high-precision air gap control and favorable disturbance resistance ability.

Hardware-in-the-loop simulation and implementation of a fuzzy logic controller with FPGA: case study of a magnetic levitation system

2018 ◽  
Vol 41 (8) ◽  
pp. 2150-2159 ◽  
Author(s):  
Onur Akbatı ◽  
Hatice Didem Üzgün ◽  
Sirin Akkaya
Keyword(s):  
Fuzzy Logic ◽  
High Speed ◽  
Fuzzy Logic Controller ◽  
Magnetic Levitation ◽  
Order System ◽  
Membership Functions ◽  
Inference System ◽  
Levitation System ◽  
Magnetic Levitation System

This paper presents the design and implementation of a fuzzy logic controller using Very High Speed Integrated Circuit Hardware Description Language (VHDL) on a field programmable gate array (FPGA). First, a Sugeno-type fuzzy logic controller with five triangular and trapezoidal membership functions for two inputs and with nine singleton membership functions for one output is examined. The proposed structure is tested with second- and third-order system model using FPGA-in-the-loop simulation via a MATLAB/Simulink environment. Then, for different kinds of fuzzy logic controllers, a new look-up table (LUT) and interpolation-based controller implementation is proposed to eliminate the computational complexity of the primarily designed structure. As a case study, a magnetic levitation system is controlled with an adaptive neuro-fuzzy inference system (ANFIS) trained fuzzy logic controller, then it is simulated and implemented using a LUT-based controller. Finally, we provide a comparison of results.

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