Control Of Magnetic Levitation System Research Articles

Optimal dynamic control for a maglev vehicle moving on multi-span guideway girders

Abstract An optimal control algorithm using a virtual tuned mass damper called virtual TMD to control the levitation force of a magnetic system is developed for resonance suppression of a maglev vehicle moving on multi-span guideway girders. Since the optimal dynamic parameters of a TMD in vibration control are well developed, the optimal tuning gains required to control the magnetic oscillations of the maglev bogie can be directly used and fed back to the maglev control system. To address the dynamic interaction analysis from the maglev vehicle to the guideway girders and vice versa, the entire coupling system is decomposed into two subsystems, one is the moving vehicle subsystem and another the stationary guideway subsystem. Then, an incremental–iterative procedure associated with the Newmark method is presented to solve the two sets of subsystem equations. Finally, the control effectiveness and parametric studies of the optimal virtual TMD scheme on resonance reduction of the moving maglev vehicle are demonstrated.

Adaptive neural network control for maglev vehicle systems with time-varying mass and external disturbance

Unexpected disturbance and ever-changing passengers are unfavorable factors that always accompany maglev trains. If not considered or handled properly, they would deteriorate the control system performance significantly and even cause instability. This paper proposes a neural network-based adaptive control approach to stabilize the airgap of the nonlinear maglev vehicle. Meanwhile, the time-varying mass and external disturbance can be estimated accurately. Specifically, to ensure the asymptotic stability of the maglev system, a nonlinear basic control law is developed first. To tackle the uncertainty, a radial basis function neural network is fused into the basic controller, which can recover the unknown mass and disturbance more quickly and accurately. Lyapunov stability techniques are utilized to prove the stability of the whole maglev control system without any linear approximation. The sufficient comparative simulation results are provided to demonstrate that the established control scheme can obtain better levitation performance and achieve a precise estimation of time-varying and disturbance simultaneously. Finally, we build a dSPACE-based single electromagnet suspension test bed to examine its efficacy and practical applicability as well.

Enhanced Model Reference Adaptive Control Scheme for Tracking Control of Magnetic Levitation System

Magnetic Levitation is a process in which an object is suspended with the support of the magnetic field. Despite being an unstable system, Magnetic Levitation Systems (MAGLEV) have profound applications in various fields of engineering. MAGLEV systems are sensitive, unstable, and nonlinear and uncertainties always pose a challenge in Controller Design. As a solution, adaptive controllers came into existence with adaptation mechanisms to cover the system uncertainties. In this study, a simple, novel, and an effective approach to the Enhanced Adaptive Control scheme is proposed for the ball position control and tracking of an unstable Magnetic Levitation System. The proposed Enhanced Model Reference Adaptive Scheme (EMRAC) follows the same phenomenon of the Model Reference Adaptive Scheme (MRAC) with a slight difference in its control strategy. The proposed scheme consists of Proportional-Integral-Velocity plus Feed Forward as the control structure and a modified version of the standard tuning rule is used as the adaptation mechanism. The control scheme is applied to a standard benchmark Magnetic Levitation System and the tracking performance of the scheme is tested by applying square and multi-sine pattern trajectories to the Magnetic Levitation System. The performance of the developed Enhanced MRAC performance is compared with that of the Proportional Integral Velocity with Feedforward Control (PIV+FF) scheme and the proposed control scheme is proven to be more suitable. The performance of the proposed scheme is also analyzed with Power Spectral Density and Root Mean Square Error to evaluate the ball position tracking control. It is inferred from the experimental results that Enhanced MRAC accommodates the changes and makes the system more reliable with good tracking ability.

Model and control of magnetic levitation system

En este artículo se realizó el modelado y control de un sistema de levitación magnética ( MAGLEV). Para ello se construyó un prototipo con materiales de bajo costo con el objetivo de realizar prácticas experimentales para la enseñaanza de sistemas de control en el programa de Ingeniería Física. Los resultados muestran que es posible utilizar el sistema para controlar la posición de una bola mediante una estrategia de control lineal.

Design and characteristics of a new aerostatic bearing stylus sensor for surface measurement

Stylus profilometry is extensively employed in practice due to its wide measurement range and praiseworthy stability. However, its lever structure will cause nonlinear errors and mitigated precision. Given this context, this paper proposes a new aerostatic bearing stylus sensor (ABSS) for surface measurement, which inherits the stability and large-scale measuring range while avoiding the structural nonlinear errors of the conventional stylus profilometry. The designed ABSS is composed of a magnetic levitation control system and an aerostatic bearing device in the vertical and horizontal directions, respectively. Furthermore, the bearing capacity and the radial dynamic characteristics are analyzed to ensure stylus’ scanning stability and rapid response. Template experiments with different scanning velocities of the stylus are performed to verify the natural frequency and obtain the critical velocity. Finally, the surface two/three-dimensional topographies reconstruction validates the repeatability and accuracy of the proposed ABSS compared to a commercial conventional stylus profilometry.

Design of PID Controller for Magnetic Levitation System using Harris Hawks Optimization

In most real-time industrial systems, optimal controller implementation is very essential to maintain the output based on the reference input. The controller design problem becomes a complex task when the real-time system model becomes greatly non-linear and unstable. The proposed research aims to design the finest PID controller for the unstable Magnetic Levitation System (MLS) using the Harris Hawks Optimization (HHO) algorithm. The MLS is a highly unstable electro-mechanical system and hence the design of the controller is a complex task. The proposed work implements one Degree of Freedom (1DOF) and 2DOF PID for the system. In this work, the essential controller is designed with a two-step process; (i) Initial optimization search to find the P-controller (Kp) gain to stabilize the system and (ii) Tuning the integral (Ki) and derivative (Kd) gains to reduce the deviation between the reference input and MLS output. The performance of the proposed controller is validated with the servo and regulatory operations and the result of this study confirms that the proposed method helps to get better error value and time domain specifications compared to other available methods.

Sliding mode controller for magnetic levitation system comparatives studies

The objective of this paper is to synthesize a regulator based on sliding modes for the control of a magnetic levitation system and to compare the performances of this type of regulator with other regulators namely: PID, fuzzy regulator.
 The simulation results show the merits of the sliding mode technique since it is robust against disturbances and parameter changes in the model. The advantages and disadvantages of each regulator are outlined in the form of a simulation curves.

Control of Magnetic Levitation System suspended by Elastic Element

Control of Magnetic Levitation System suspended by Elastic Element

Vertical direction conveyance control of the magnetic levitation control system using the electromagnet with the embedded displacement sensor

Vertical direction conveyance control of the magnetic levitation control system using the electromagnet with the embedded displacement sensor

Simulation and Test of Energy Consumption of High-Tc Superconducting Coils in Electromagnetic Levitation System

In order to evaluate the energy consumption of the superconducting magnetic levitation system with real-time control, the methods of simulation and test were used to study the AC plus DC loss of the superconducting coil. The simulation established a 2D finite element model in COMSOL, which was based on the H equations method with simple form and fast calculation speed. The test was carried out on a new type of measurement system, which adopted the electrical method with high sensitivity, simple operation and easy implementation. The results of simulation and test showed that, under normal working conditions, the levitation current in the form of AC plus DC produced a relatively small energy loss below $1mW$ per meter. This conclusion is helpful for the promotion and application of superconducting magnetic levitation control system.

Model predictive control of magnetic levitation system

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.

Reduced-order Generalized Proportional Integral Observer Based Continuous Dynamic Sliding Mode Control for Magnetic Levitation System with Time-varying Disturbances

In order to reduce the influence of time-varying disturbances for magnetic levitation system, we propose a reduced-order generalized proportional integral observer (RGPIO) based continuous dynamic sliding mode control scheme for magnetic levitation system in this paper. Unlike the popular extended state observer (ESO), it could deal with constant or slowing varying disturbances from theoretical point of view, the reduced-order generalized proportional integral observer (RGPIO) is designed to estimate the time-varying disturbances and system states, then the dynamic sliding mode surface is developed and deduce a continuous sliding mode controller (CSMC) for magnetic levitation system. Compared with ESO based continuous sliding mode controller, the proposed method not only ensures the position tracking accuracy, but also obtain better time-varying disturbance reject ability. Simulation and experimental results are also given to verify the effectiveness.

Artificial Neural Network vs PID Controller for Magnetic Levitation System

This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and the electromagnetic force controlled by varying the supply voltage. To control this nonlinear system, we develop a multilayer artificial neural network by using Matlab software and integrate that with the physical magnetic levitation model. According to specific initial conditions, the actual responses of the magnetic levitation system with artificial neural network compares the desire response of the metal sphere. The ability of control this nonlinear system by using neural networks validate by comparing results obtained by the PID controller and artificial neural network.

Fuzzy Sliding Mode Control of Magnetic Levitation System of Controllable Excitation Linear Synchronous Motor

In order to improve the ability of magnetic levitation feed platform of controllable excitation linear synchronous motor (CELSM) to resist external disturbance, a sliding mode control method based on fuzzy switching gain adjustment is proposed. According to the operation mechanism of CELSM, the voltage equation, flux equation, magnetic levitation force equation, and state equation of CELSM are established. The sliding mode surface is designed, and the integral term is introduced to eliminate the steady-state error of the system. The reaching law is designed to reduce chattering by using the saturation function instead of the sign function. Fuzzy logic reasoning is used to estimate the switching gain, to cancel the unknown disturbance, and further weaken the chattering of the system. The sliding mode controller based on fuzzy switching gain adjustment is designed, and the system is simulated in MATLAB environment. The simulation results show that the fuzzy sliding mode control algorithm can improve the dynamic performance of the system and weaken chattering. The feasibility of this algorithm is proved. The experimental platform of CELSM magnetic levitation system is built to carry out partial experimental research on the magnetic levitation control system.

Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy

In this paper, a novel approach is proposed for adjusting the position of a magnetic levitation system using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The principles of designing magnetic levitation systems have widespread applications in the industry, including in the production of magnetic bearings and in maglev trains. Levitating a ball in space is carried out via the surrounding attracting or repelling magnetic forces. In such systems, the permissible range of the actuator is significant, especially in practical applications. In the proposed scheme, the procedure of designing the backstepping control laws based on the nonlinear state-space model is carried out first. Then, a constrained optimization problem is formed by defining a performance index and taking into account the control limits. To formulate the recurrent neural network (RNN), the optimization problem is first converted into a constrained quadratic programming (QP). Then, the dynamic model of the RNN is derived based on the Karush-Kuhn-Tucker (KKT) optimization conditions and the variational inequality theory. The convergence analysis of the neural network and the stability analysis of the closed-loop system are performed using the Lyapunov stability theory. The performance of the closed-loop system is assessed with respect to tracking error and control feasibility.

Deep learning controller design of embedded control system for maglev train via deep belief network algorithm

The maglev train has been successful in practice as a new type of ground transportation. Owing to the inherent nonlinearity and open-loop instability of the electromagnetic suspension (EMS) system, an analogue or a digital controller is used to control the maglev trains’ stability. With the rapid development of embedded systems and artificial intelligence, intelligent digital control has begun to replace the conventional analogue control technology creating a new approach to the EMS control system. This paper proposes a hardware module for an embedded levitation controller based on digital signal processor and field programmable gate array, hence producing an open loop mathematical model of the embedded maglev control system. The deep learning controller is then developed based on a deep belief network (DBN) algorithm and a proportional integral derivative feedback controller. The simulations are conducted in the MATLAB environment after training the DBN. Simulation results are compared with those obtained from the conventional controller. Finally, experiments are implemented to examine the feasibility in practice of the application of the DBN into a maglev embedded control system. The system, with the proposed controller, can accurately track the target airgap of 8 mm. The maximum tracking error of sinusoidal trajectory is 0.17 mm and the maximum tracking error of step trajectory is 0.98 mm. Both simulation and experimental results are included in this paper to show that the proposed deep learning controller can be more robust and less complicated to implement in maglev control applications.

Analysis of Gap Time-Delay in Maglev Control System

In the maglev control system, signal filtering, A/D sampling and communication all bring time-delay to the gap signal, which will affect the performance of the levitation control. In order to analyze this effect, a single-degree-of-freedom maglev system model is established. The state feedback control method is used to control the maglev system, and the stability of the closed-loop system is analysed and he stable region of control parameters is studied. Gap time-delay is introduced into the closed-loop system model, and the influence of time-delay on the stability range of control parameters is analyzed. It is concluded that the gap time-delay will reduce the stability range of control parameters, the larger the time-delay of position signal is, the smaller the lower limit of the stability region of differential feedback coefficient is and the smaller the stability region is. The simulation results are used to verify the validity of the theoretical analysis.

Comparison of Ziegler-Nichols and Cohen Coon tuning method for magnetic levitation control system

Magnetic levitation is an example of a nonlinear system that is naturally unstable. In the control system field, it can be also used to check the effectiveness of control methods. Unlike other researches overcoming by analytical method, this research investigates the solution of the nonlinearity of the magnetic field by placing two effect hall sensors on the top and bottom of magnetic coils. After decreasing the effect of nonlinearity, two PID control tuning methods, Ziegler Nichols (ZN) and Cohen Coon (CC), are compared to obtain an appropriate control structure and its initial parameters. The experimental result shows that the best control structure of ZN method is PID whereas its initial parameters, Kp, Ti, and Td, are 15.33, 0.036 and 0.009, respectively. The rise-time and settling time of the response are 0.54s and 0.59s. The appropriate control structure is PI control whereas Kp and Ti are 30.05 and 0.012 from CC method. The rise-time and settling time resulted are 0.8s and 0.67s. The ZN method with PID control structure has a better response with smaller rise-time and settling time than PI control from CC Method. This research is benefit for solving a problem dealing with magnetic levitation control.

Investigation on planar electromagnetic levitation system using lead compensation and LQR controllers

Magnetic levitation is a method by which an object is suspended with no support other than magnetic fields. The main objective of this study is to demonstrate stabilized closed-loop control of 2-DOF maglev experimentally using real-time control Simulink feature of (SIMLAB) microcontroller. Phase lead compensation and linear quadratic regulator (LQR) controllers are employed to examine the stability performance of the maglev control system under effect of sudden wave signal change and load on maglev plane. The effect of sudden change of applied wave signal on single point, line and plane is presented. Furthermore, in order to study the effect of sudden change of applied load, the direct full load has been applied on all points of the prototype maglev plate simultaneously. Moreover, the airgap distance controlled using phase lead compensation controller is unstable with high oscillation. Meanwhile, LQR controller provided more stability, homogeneous response and good agreement. Additionally, the results of pulse width modulation reveal that the control system using LQR controller provides identical and smooth response to adjust the levitated plane compared to phase lead compensation controller.

Design and Control of Magnetic Levitation System by Optimizing Fractional Order PID Controller Using Ant Colony Optimization Algorithm

MAGnetic LEVitation (Maglev) is a multi-variable, non-linear and unstable system that is used to levitate a ferromagnetic object in free space. This paper presents the stability control of a levitating object in a magnetic levitation plant using Fractional order PID (FOPID) controller. Fractional calculus, which is used to design the FOPID controller, has been a subject of great interest over the last few decades. FOPID controller has five tunning parameters including two fractional-order parameters ($\lambda $ and $\mu $ ). The mathematical model of the Maglev plant is obtained by using first principle modeling and the laboratory model (CE152). Maglev plant and FOPID controller both have been designed in MATLAB-Simulink. The designed model of the Maglev system can be further used in the process of controller design for other applications. The stability of the proposed system is determined via the Routh Hurwitz stability criterion. Ant Colony Optimization (ACO) algorithm and Ziegler Nichols method has been used to fine-tune the parameters of FOPID controller. FOPID controller output results are compared with the traditional IOPID controller for comparative analysis. FOPID controller, due to its extra tuned parameters, has shown extremely efficient results in comparison to the traditional IOPID controller.