Design of a nonlinear controller using feedback linearisation based on backstepping technique for magnetic levitation system

2016 ◽  
Vol 2 (4) ◽  
pp. 247
Author(s):  
Bhawna Tandon ◽  
Shiv Narayan ◽  
Jagdish Kumar
Keyword(s):  
Magnetic Levitation ◽  
Backstepping Technique ◽  
Nonlinear Controller ◽  
Levitation System ◽  
Magnetic Levitation System

scholarly journals Model predictive control of magnetic levitation system

2020 ◽  
Vol 10 (6) ◽  
pp. 5802
Author(s):  
Lafta E. Jumaa Alkurawy ◽  
Khalid G. Mohammed
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Controller Design ◽  
Magnetic Levitation ◽  
Lagrange Function ◽  
Sufficient Conditions ◽  
Nonlinear Controller ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
The Stability

In this work, we suggest a technique of controller design that applied to systems based on nonlinear. We inform the sufficient conditions for the stability of closed loop system. The asymptotic stability of equilibrium and the nonlinear controller can be applied to improvement the stability of Magnetic Levitation system(MagLev). The MagLev nonlinear nodel can be obtained by state equation based on Lagrange function and Model Predictive Control has been used for MagLev system.

scholarly journals A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 332
Author(s):  
Nihal Dalwadi ◽  
Dipankar Deb ◽  
S. M. Muyeen
Keyword(s):  
Adaptive Control ◽  
Control Structure ◽  
Magnetic Levitation ◽  
Reference Model ◽  
Tracking Error ◽  
Transportation System ◽  
Nonlinear Controller ◽  
Maglev Transportation System ◽  
Levitation System ◽  
Magnetic Levitation System

Maglev transportation system is become a hot topic for researchers because of the distinctive advantages, such as frictionless motion, low power consumption, less noise, and being environmentally friendly. The maglev transportation system’s performance gets sufficiently influenced by the control method and the magnetic levitation system’s dynamic performance, which is a critical component of the maglev transportation system. The Magnetic Levitation System (MLS) is a group of unstable, nonlinear, uncertain, and electromagnetically coupled practical application. Control objective of this study is to design a position stabilizing control strategy for Magnetic Levitation system under extreme uncertain parametric conditions using a reference model governed by a reference stabilizer and nonlinear adaptive control structure. After successful tuning the reference stabilizer with and without time-varying payload disturbance, the tracking-error dynamics are obtained in the presence of both matched and mismatched types of parametric uncertainties. Next, the close-loop stability theorem is formulated for Lyapunov stability analysis to get the design constraints, parameter update laws, and adaptive control law. Numerical simulations performed for a high range of parametric violations check the control design’s efficacy. The performance robustness gets confirmed by comparing the results with the nonlinear control approach. The MLS gets performance recovery and settles within safe limits in few seconds using the proposed methodology. However, the nonlinear controller faces permanent failure in stabilizing the MLS.

scholarly journals Movement Control Method of Magnetic Levitation System Using Eccentricity of Non-Contact Position Sensor

2021 ◽  
Vol 11 (5) ◽  
pp. 2396
Author(s):  
Jong Suk Lim ◽  
Hyung-Woo Lee
Keyword(s):  
Motor System ◽  
Control Method ◽  
Magnetic Levitation ◽  
Movement Control ◽  
Position Sensor ◽  
Semiconductor Wafer ◽  
Control Command ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Contact Position

This paper presents a method of utilizing a non-contact position sensor for the tilting and movement control of a rotor in a rotary magnetic levitation motor system. This system has been studied with the aim of having a relatively simple and highly clean alternative application compared to the spin coater used in the photoresist coating process in the semiconductor wafer process. To eliminate system wear and dust problems, a shaft-and-bearing-free magnetic levitation motor system was designed and a minimal non-contact position sensor was placed. An algorithm capable of preventing derailment and precise movement control by applying only control without additional mechanical devices to this magnetic levitation system was proposed. The proposed algorithm was verified through simulations and experiments, and the validity of the algorithm was verified by deriving a precision control result suitable for the movement control command in units of 0.1 mm at 50 rpm rotation drive.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

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