# Magnetic Levitation System

## Top PDF Magnetic Levitation System: ### Optimization of Magnetic Levitation System

Keywords:- Magnetic Levitation, Controller, Optimization, Particle swarm optimization. I. INTRODUCTION In electromagnetic attraction type magnetic levitation system, the levitated object attains steady position when magnetic force and gravitational force are equal and opposite. Hence proper flow of current through the electromagnet coil is essential in suspending the object at the desired position. This is achieved by a controller. For applications like bearingless motors (Chiba 1995), maglev (Taniguchi 1992) etc where „Y‟, the distance between the coil and the suspended object has to remain constant, the nonlinear system can be linearized and represented by a transfer function as presented by Trumper 1997,Shiao 2001 etc. By using a proper controller, the current through the coil can be made a function of „Y‟ and the system can be stabilized. ### Magnetic Levitation System: Modeling And Control

CHAPTER 1 INTRODUCTION 1.0 Background Magnetic levitation system is a method to levitate object by using electromagnetic force only. The force from the magnetic field counteract with the gravitational force which make the object float. Magnetic levitation is not a new thing in engineering in fact Robert Goddard and Emile Bachelet in (1990’s) is the earliest people that seen the theory of magnetic levitation. They envision coming up with frictionless transportation system using repulsive forces generated by alternating current. But the system is put on hold because it uses too much power for conventional conductors. ### Robust Positioning Control Of Magnetic Levitation System

For magnetic levitation system, there are two way can be implemented to control the system. The ways are analogue mode control and digital mode control . For analogue mode control, the magnetic levitation system can operate itself by using RC circuit. Therefore, it can work independently without using any computer software. On the other hand, digital mode control is based on computer software. For example, the magnetic levitation system can operate with Matlab and simulink software. The advantage of using digital mode control if compare with analogue mode control is digital mode control will be more users friendly and easier to analyze the performance of the system. Apart from that, digital mode control also can use to determine the parameters of the system and use it to model the system. Basically, Matlab and simulink are used for simulation and develop control algorithm based on the design requirement. Therefore, it will be easier to implement different control algorithm into the system and analyze the transient performance of the system with Matlab and simulink. Therefore, digital mode control will be implemented in this project rather than using the analogue mode to analyze the performance of the magnetic levitation system. ### 5. Controller Optimization of Magnetic Levitation System

Controller parameters are important components of the factors which affects the performance of controllers. It may not be possible to adjust these parameters with higher efficiency and performance using classical methods. Therefore, various optimization algorithms are used for optimizing the controller parameters. Abstract: Magnetic levitation systems are one of the popular structures in control applications. These systems are non-linear and it is possible to observe the performance of various types of controllers on these systems. Nowadays there are application areas like maglev train technologies, vibration isolation systems etc. While designing a controller, determination of the controller coefficients is complex, and the classical methods require long time for tuning process. With the help of optimization algorithms, the parameter tuning process of the controllers can be done in short time and it is possible to take an optimum performance from the controller. In this study, a magnetic levitation system is modeled and linearized. PID controller is designed for position control of the linearized model. The PID controllers are widely used in the industry and have enough performance for many applications. Increasing the performance of a PID controller is possible with optimization techniques. The coefficients of the designed controller are tuned using the Genetic Algorithm and the optimum values are found. As a result, the system’s performance is developed sufficiently with short settling time and small overshoot. ### Observer Based Controller for Magnetic Levitation System

1 pnattest@gmail.com, 1 junaid.ee@suit.edu.pk, 2 sadaqat.ee@suit.edu.pk ABSTRACT This paper explains Magnetic Levitation system of a train which comprises of guidance track made with magnets. The main objective is to design a proper controller that can suspend and propelled the train on a guidance track made with magnets. To perform the desired task state space model of Magnetic Levitation system is derived. The response of the system is simulated in MATLAB. The open loop response showed that the derived model is unstable. Observer Based Controller (OBC) is designed to analyze the system in closed loop. The controller showed improved performance for different tracks. Different types of realization techniques (minimal realization, balanced realization, modal realization, observer canonical realization) are compared for minimum fragility in controller implementation. ### IMC Based PID Control of a Magnetic Levitation System

Attraction type magnetic levitation devices are nonlinear and unstable systems with fast dynamics. If a model of such a system can be produced, it could be used in the design process of a stabilizing controller. Internal Model Control (IMC) provides a strategy that explicitly uses an existing model of the controlled process for developing a suitable controller. In this paper, a linear model that represents the nonlinear dynamics of the magnetic levitation system is first derived. Then, this model is used in the design procedure of an IMC-based PID controller, which is used for achieving stable levitation of a ferromagnetic object at predetermined distances with the help of the magnetic field produced by a coil. The results are shown by means of digital simulation, based on Simulink. ### IMPLEMENTATION OF NARMA-L2 CONTROLLER FOR MAGNETIC LEVITATION SYSTEM

1,2 Department of Electronics Engineering University College of Engineering, Rajasthan Technical University, Kota, Rajasthan ABSTRACT This paper presents the design of feedback linearization and neural network based feedback linearization (NARMA- L2) controller for a magnetic levitation system. The magnetic levitation system is one of the classical nonlinear systems. The paper provides simulation results to validate the theoretical design. ### Modeling of a compliant joint in a Magnetic Levitation System for an endoscopic camera

3 STORM Lab, Mechanical Engineering Department, Vanderbilt University, Nashville, TN, USA Correspondence to: M. Simi (m.simi@sssup.it) Received: 10 March 2011 – Revised: 6 July 2011 – Accepted: 15 December 2011 – Published: 18 January 2012 Abstract. A novel compliant Magnetic Levitation System (MLS) for a wired miniature surgical camera robot was designed, modeled and fabricated. The robot is composed of two main parts, head and tail, linked by a compliant beam. The tail module embeds two magnets for anchoring and manual rough translation. The head module incorporates two motorized donut-shaped magnets and a miniaturized vision system at the tip. ### Sliding Mode Control with Gain-Scheduled for Magnetic Levitation System

1. INTRODUCTION Magnetic levitation or maglev system is a technique to make objects can float in the air using magnetic fields. The force produced by the magnetic field counteracts the gravitational force in order to the object does not fall. The Maglev system has advantages such as no friction with other objects so that the efficiency of movement becomes faster and requires low energy consumption. For example, the maglev train system can run at a top speed of 603 kilometers per hour  . However, the maglev system has high nonlinear dynamics, where nonlinear systems are more challenging to control than linear systems.The research conducted was controlling an object that can float a stable at a certain height. Illustration of the maglev system shows in Fig. 1 . The system requires an electromagnetic coil, around iron object, a sensor (phototransistor and LED to detect the position of an object), a driver (to adjust the current and voltage needed by an electromagnetic coil), a microcontroller (as a controller). Several studies have used various methods to control object of a maglev system, including controlling maglev objects using sliding mode control  , but the maglev equation is changed from nonlinear to linear so that if implemented on the plant the controller will not be optimal. Then, control of a magnetic levitation system using PD (Proportional Derivative) and PID (Proportional Integral Derivative) controller  . The PID control system demonstrates better performance in steady-state error and settling time rather than PD control system. However, this paper did not consider disturbance in magnetic levitation. In addition, there are also other methods such as fuzzy logic controller  , feedback linearization ### Digital Control of Magnetic Levitation System using Fuzzy Logic Controller

4. RESULTS AND DISCUSSION The results shows that the controlled electromagnetic current can stabilize the disturbance that otherwise would cause the metal sphere to fall or attach itself to the electromagnet. In case of PID controller the integral action in magnetic levitation system can improve the system performance in terms of error minimization. However because of the fact that the system is not self starting the integral action has to be turned on when the ball acquires the stabilized position by the PD controller. ### Comparative Analysis of MIT Rule and Differential Evolution on Magnetic Levitation System

Abstract—In high accuracy applications such as designing autopilot system for aircrafts, missile guiding systems and in various fields for robotics and automation, one needs to design the control system with more powerful and advanced techniques so as to maintain the satisfactory performance of the overall system. The paper presents the comparative analysis of MIT rule based control with Differential Evolution (DE) algorithm based control by implementing them on magnetic levitation system in real time. It also shows the development of adjustment mechanism with necessary mathematics using gradient algorithm based MIT rule along with the mathematical modeling of magnetic levitation system. The simulations have been performed using MATLAB, and a comparative study among two strategies has been done based on these simulations. The performance of the developed controllers has been evaluated on magnetic levitation system in real time, which suggests that DE based offline tuned PID controller performs comparatively better than MIT rule based online tuned PID controller. Results also depict that MIT rule control is very sensitive to parameter variations, whereas DE based control shows robust performance. ### Proportional-Integral-Derivative Gain-Scheduling Control of a Magnetic Levitation System

PID control and PID-GS-C structures are next designed to ensure zero steady-state control error and bumpless switching between PID controllers for the linearized mod- els. Real-time experimental results are presented for validation. Keywords: gain-scheduling, magnetic levitation system, Proportional-Integral- Derivative control; real-time experiments. ### Design & Control of Magnetic Levitation System ED 4810: Review and Stability Test ### An Undergraduate Control Tutorial on Root Locus-Based Magnetic Levitation System Stabilization

IV. C ONCLUSION Stabilization of a magnetic levitation system has been the focus of this paper. Although the system is an unstable nonlinear one, it is clear that a linear compensator can be designed to stabilize it if its operation is limited to a small range (although this greatly limits the robustness of the compensator). We develop a complete nonlinear model of the system, and then form an approximate linearized equivalent from it. Based on this linearized model, we consider two linear compensators—proportional-derivative and phase lead—and show that the magnetic levitation s ystem can be stabilized by an appropriate selection of the parameters of the compensators using a classical design approach aided by a computer software tool. We compute and present the closed -loop poles of each design and the corresponding step responses, and also show the system stability limits. This approach proves quite useful and effective, as several simulation runs can be performed quickly to expedite the design. However, for a large-range operation, a more robust controller will be required to effectively bring the system into a region of stability. And for this latter type of controllers, several strategies, such as sliding mode control, adaptive control, etc., have been employed and are available in the literature, while the maglev system continues to attract more research attention. ### Robust H∞ control of Magnetic Levitation system based on parallel distributed compensator

The aim of this paper is to propose a robust controller to highly nonlinear Magnetic Levitation system using TSF model control in the discrete form. By using the PDC concept, a H 1 controller will be applied to Maglev exposed to external distur- bances. Thus, the proposed controller is TSF control system that the stability analysis is achieved and then LMI conditions are presented to calculate the gain of the controller. The nov- elties here is that a proposed controller will robustly stabilize the Maglev system for both current-controlled and voltage- controlled schemes exposed to external disturbances using straightforward algorithms. Finally, simulation results are given to prove that the proposed technique ensures the stabil- ity conditions and guarantees robustness against external dis- turbance for a complex nonlinear system. The paper is organized as follows. First, the related work is presented in Section 2 . Then some preliminaries and problem formulation are given in Section 3 . Section 4 shows the fuzzy controller design. In Section 5 , the simulations are given to emphasize the efficiency of the proposed method and compare it with other PDC schemes for voltage and current controlled Maglev system. Finally, the paper is concluded in Section 6 . ### Real Time Implementation of Series Expansion Based Digital Controller for Magnetic Levitation System

Abstract This paper addresses a digital controller for a real time magnetic levitation system using series expansion of pulse transfer function, which achieves desired closed loop response. The proposed digital controller designed, based on series expansion of pulse transfer function by solving a linear equation using the method of least squares, which improves the transient performance and step tracking capability of the com- pensated system. The designed algorithm used for the control input is not iterative, so the calculation is very fast. The proposed control scheme has successfully applied on maglev system and also validated through the simulation and hardware experi- mental results. ### Model predictive control of magnetic levitation system

Figure 13. The amplitude at 𝛤 𝑦 = 2, 𝛤 𝑢 = 10 6. CONCLUSION The work applied in this paper was designed the dynamic magnetic levitation of nonlinear system that was investigated in terms of its modeling, simulation and linearization. Because of the non-linear and unstable and featur of magnetic levitation systems, in this paper, we tried to convert the nonlinear to the linearize model by analyzing with state-space and doing a simulation. Three controllers applied to the system to check what it is the best controller. The first and second classic control were PI controller and PID controller and these controllers were not satisfied for stability and steady state error. The third controller was MPC to predict the future response related to reference input and this controller was satisfied for stability, get zero steady state error and fast response with minimum settling time. Further, a new research uses linear quadratic form to control the magnetic levitation syatem to ensure the stability with changing the input. ### Modeling and Vibration Control with a Nanopositioning Magnetic-Levitation System

the MIMO system. An LQG/LTR controller is designed to reject vibration disturbance in a MIMO system. To derive a more accurate model of the maglev system, a dynamic model of the optical table with a pneumatic passive vibration isolator is also considered. The stiffness and damping parameters of the optical table are obtained from an impulse response. The magnetic stiffness has been added by the linearization of the EOMs of the optical table and the platen in vertical motion. The enhanced model is validated by comparing between the simulations and the experiments. To test the performance in the vibration control, an unbalanced vibrating motor is designed for generating vibration disturbances. The dual-loop control system with velocity feedback makes it possible that the maglev system tracks positioning commands and rejects vibration disturbance simultaneously. The design procedures of the dual-loop control systems are introduced in vertical and horizontal motions. The inner-loop compensator regulating the velocity is developed for vibration rejection, and the outer-loop compensator is designed to position of the platen. The stabilizing regions of the gain values for vibration-rejecting compensators are analyzed. The capacitance probes as vertical motion sensors do not directly provide the velocity information. The velocity in vertical motion is obtained by differentiating the noisy position signals. A software low-pass filter is designed to prevent amplification of the high frequency noise of position signal by differentiation. ### Characterization and adaptive fuzzy model reference control for a magnetic levitation system

also, to bound the rule adaptation it is used a small number  jpðkTÞj <  ! pðkTÞ ¼ 0: ð35Þ In this way, the output of the inverse model pðkTÞ displaces the center of the membership function fired dT sampling times before, due the change in the control signal takes dT seconds in modify the system behavior. For stability issues, the centers displacements are bounded with ½b min ; b max  ¼ ½0; g v , some results are reported for stability in fuzzy systems, they are for Takagi–Sugeno fuzzy systems [30,31], where a fuzzy model is used to approach the real system with linear subsystems in the rules consequents, then by using Lyapunov’s theory, stabil- ity can be guaranteed; a similar procedure to the presented in this paper was shown by Passino and Yurkovich  and then compared with a Lyapunov-based model reference adaptive control, but this last method presents an important overshoot; 