CHAPTERLiterature Survey

2Roberto Kawakami Harrop Galvo et.al [2003] describes a simple technique for obtaining a linearized model for a magnetic levitation system using input/output measurements was described. It was shown that the proposed model structure is well suited to represent experimental data. The nonlinear nature of the system is made apparent by identifying parameters for different equilibrium points. In fact, parameter estimates and varies by 13% and 23%, respectively, between the extreme points considered, showing that the dynamic behaviour of the plant depends on the region of operation.Faa-Jeng Lin et.al[2007] describes an intelligent adaptive back stepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a magnetic levitation apparatus to compensate for uncertainties, including friction force. Then, a suggestion for an adaptive back stepping approach to compensate disturbances, including the friction force, occurring in the motion control system is approached. To further increase the robustness of the magnetic levitation apparatus an RNN estimator for the required lumped uncertainty in the adaptive back stepping control system is designed.Adrian-Vasile Duka et.al [2008] gives a Fuzzy-PD control strategy that evolved from heuristic knowledge and classical control methods were presented in this paper. The new approach improved the tracking accuracy for the closed loop nonlinear electromagnetic levitation process. It achieved this with very fast convergence speed, it extended the working conditions outside the initial linear domain, it presented robustness against model uncertainties and allowed very precise positioning of the levitated object.

JieMa et.al [2008] describes the non-linear features of the maglev system, a fuzzy PID controller through the combination of traditional PID control and fuzzy control technology is designed using the MATLAB software. Simulation results show that the step response of ball position has less response time and less overshoot. However, because of the complex algorithms of fuzzy control, we have to do much more calculation. All of the above are in the higher need of the processing speed of the hardware and real-time operation.Hong QIU et.al [2009] presents a magnetic levitation ball control system based on TMS320F2812. And this paper also proposes a feasible and effective solution to the problem of sampling instability that generally exits in the TMS320F2812 A/D module. The magnetic levitation ball is suspended successfully by the Designed controller.A.K. Ahmad et.al [2010] demonstrates for the fuzzy controller, the response is slower than the PID controller. However, fuzzy controller shows the best performance in terms of lowest overshoot among three controllers and no steady state error. It reaches the desired set point at t = 0.98 sec. The PID controller has steady state error and with the best adjustment, it reaches to the desired set point. Scaling factors are most important with respect to fuzzy controller performance and provide a guideline for tuning. It was shown that the scaling factors play a role similar to that of the gain coefficients for conventional controllers.Wenbai Chen et.al[2010] designed, according to chaos theory, a new method of PI D controller parameters turning based on chaos optimization is presented and applied to magnetic levitation ball control system. The experimental results show that the steel ball had been successfully levitated by the use of PID controller. Comparing with the primary controller without optimization, the control performance of the optimized PI D controller is improved. That is, the PI D parameter tuning strategy in this paper is feasible and effective.Kumar et.al[2012] presents an interval type-2 single input fuzzy logic controller for a magnetic levitation system. The objective of this work was to design a stabilizing controller for MLS and this has been successfully achieved. The IT2SIFLC controller is proved to be effective and feasible. The effect of the control can be easily seen from the simulation and experimental test results. The results shows better performance of the proposed controller compared to an IT2FLC and TlFLC controller. The proposed controller does not require heavy computations and less processor complexity therefore, implementation is feasible. The performance comparison is summarized in table IV and clearly indicates that the proposed controller is fast and giving improved performance compared to other existing controllers. In case of trajectory tracking when the reference signal is sinusoidal, tracking will be more accurate when the frequency of reference trajectory is small. Similarly, it is applicable for varying equilibrium position tracking. The next performance criterion is robustness proof for proposed IT2SIFLC controller. Type-2 fuzzy controllers give better disturbance rejection then TlFLC. The simulation results are validated with the experimental real time model of MLS, developed by googol tech.

Luiz H. S. Torres et.al [2010] Describes the combination of two techniques to control a MLS was presented: exact linearization with states feedback and fuzzy logic. It was possible to verify that the Simulated results show an overshoot in the response of the controller. However, these same results show that the controller output signal tracks a reference input signal. Hitoshi Katayama et.al [2011] describes the stabilization control of a magnetic levitation system by a back stepping technique and high-gain observers is considered. We first derive state feedback stabilizing laws by a back stepping technique. Then a high-gain observers and construct output feedback stabilizing controllers based on the designed high-gain observers is designed.Kashif Ishaque et.al [2011] demonstrates an attempt to control the position of a steel ball in a magnetic levitation system using fuzzy logic has been proposed. From the simulation results, it has been shown that the fuzzy controller can stabilize the system efficiently Also, the performance during the transient period of the fuzzy system is better in the sense that less overshoot was obtained. Moreover, the fuzzy controller provides a zero steady state error.Basil Hamed et.al [2012] demonstrates the magnetic levitation CE152 Model is used as practical example of nonlinear systems. The fuzzy controller was designed with Matlab software and this controller was tested with the CE152 Model. The fuzzy controller stabilized the magnetic levitation CE152 model under different set points. The GA optimization method was used to optimize the membership function of the inputs and output of the fuzzy controller and also to optimize the gains p_g, pi_g, ce_g, err_g and out_g, The CE152 was tested with the new membership functions and the result shows that is better than the results of old fuzzy controller under different set points.M.Valluvan et.al [2012] presents, a conventional PID is done for simulation and real time process. The model reference adaptive control is done for simulation and its response is compared with simulation response of conventional PID. Integral square error for the two types of control schemes is calculated. From the error value it is clear that MRAC gives better response when compared to Conventional PID controller.Shekhar yadav et.al[2012] demonstrates the control strategies like PID and Fuzzy logic controller are successfully designed to control the magnetic levitation system. Based on the simulation results and also the experimental results, it is recorded that the fuzzy logic controller can stabilize the system efficiently and accurately more than PID controller.Tomoaki Takao et.al [2012] describes a magnetic levitation system with a magnetic shielding effect of a high temperature superconducting (HTS) bulk is used to increase the levitation force A ferromagnetic plate is added to increase the levitation force.

P. uster and A. Jadlovsk[2012] Presents control algorithm design for nonlinear simulation model of the Magnetic levitation using the exact input output feedback linearization method and pole placement method. The proposed control algorithm together with simulation model was implemented into control structure and verified in Matlab/Simulink program language. The resulting graph shows, that output of the model tracks step change of the reference trajectory and therefore can be considered, this approach is suitable for solution problem of control for Magnetic levitation system.R. Lakshman Kumar Reddy et.al [2013] describes the advantages of this controller rather than previous presented methods is this that all possible errors of desirable air gap can be ruined when vast change or disorder accrued by choosing an integral controller .Also integral portion changes have been considered and it has been showed that correct choosing of integral portion can decrease the time necessary for steady state mode. Also correct choosing of integral portion helps to decrease system time reaching to stability mode.Niveedha K et.al [2013] This paper presents a fuzzy controller design for a magnetic levitation system. From the results obtained, it is clear that the Fuzzy Logic Controller gives a much better performance than the PID. A fuzzy control strategy that evolved from heuristic knowledge and classical control methods was presented in this paper. Fuzzy controllers make the tracking error sufficiently small enough to achieve lesser vibration and significantly improves the levitation performance without changing the controller algorithm or increasing the cost or complexity of the system enabling the set -up , a potential tool for micromanipulation and micro positioning purposes.