Control Of Magnetic Levitation System Research Articles

Magnetic levitation system control research based on improved linear active disturbance rejection

In this study, we propose an improved linear active disturbance rejection control (I-LADRC) method to achieve stable suspension of magnetic levitation balls. This method can not only solve the overshoot problem but also enhance the anti-interference capability. The control algorithm is designed, and the convergence of the control system is verified by derivation. To verify the effectiveness of the proposed control algorithm, we conduct simulation experiments and analyze the dynamic performance, tracking effect, and anti-interference ability of four controllers: sliding mode control (SMC), LADRC, longitudinal error-LADRC (LE-LADRC), and I-LADRC. Simulation results show that I-LADRC outperforms SMC, LADRC, and LE-LADRC regarding tracking effect and anti-interference ability. We also experimentally verify the control performance of I-LADRC. The experimental results show that I-LADRC has strong anti-interference ability and robust performance, which is 100% and 50% higher than LADRC and LE-LADRC, and has good dynamic performance, which verifies the effectiveness of the simulation experiment, which exhibits better robustness and dynamic performance.

A Review of Levitation Control Methods for Low- and Medium-Speed Maglev Systems

Maglev transportation is a highly promising form of transportation for the future, primarily due to its friction-free operation, exceptional comfort, and low risk of derailment. Unlike conventional transportation systems, maglev trains operate with no mechanical contact with the track. Maglev trains achieve levitation and guidance using electromagnetic forces controlled by a magnetic levitation control system. Therefore, the magnetic levitation control system is of utmost importance in maintaining the stable operation performance of a maglev train. However, due to the open-loop instability and strong nonlinearity of the control system, designing an active controller with self-adaptive ability poses a substantial challenge. Moreover, various uncertainties exist, including parameter variations and unknown external disturbances, under different operating conditions. Although several review papers on maglev levitation systems and control methods have been published over the last decade, there has been no comprehensive exploration of their modeling and related control technologies. Meanwhile, many review papers have become outdated and no longer reflect the current state-of-the-art research in the field. Therefore, this article aims to summarize the models and control technologies for maglev levitation systems following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) criteria. The control technologies mainly include linear control methods, nonlinear control methods, and artificial intelligence methods. In addition, the article will discuss maglev control in other scenarios, such as vehicle–guideway vibration control and redundancy and fault-tolerant design. First, the widely used maglev levitation system modeling methods are reviewed, including the modeling assumptions. Second, the principle of the control methods and their control performance in maglev levitation systems are presented. Third, the maglev control methods in other scenarios are discussed. Finally, the key issues pertaining to the future direction of maglev levitation control are discussed.

An observer for magnetic levitation system control based on a coefficient diagram method and backstepping

An observer for magnetic levitation system control based on a coefficient diagram method and backstepping

Radial Basis Function-based Adaptive Gain Super-Twisting Controller for Magnetic Levitation System with Time-Varying External Disturbance

Radial Basis Function-based Adaptive Gain Super-Twisting Controller for Magnetic Levitation System with Time-Varying External Disturbance

Adaptive fuzzy sliding mode control of magnetic levitation system based on Interval Type-2 Fuzzy Neural Network Identification with an Extended Kalman–Bucy filter

The electromagnet levitation control system (EMS) is the core component of the magnetic levitation train. However, its nature is characterized by an inherent instability and strongly nonlinear dynamic. Additionally, modeling uncertainties and numerous exogenous perturbations make the design of the controller even more difficult for this system. To overcome these limitations, a Fuzzy Sliding Mode Control based on Interval Type-2 Fuzzy Neural Network Identification (FSMCF2NN) is designed for the EMS system. The suggested controller has a unique feature that allows the sliding surface and controller effort to be designed by using model-free techniques. As a result, the drawbacks of the conventional SMC schemes, such as the chattering effect, requiring prior knowledge of the system’s uncertainties bounds, and selection of the appropriate control gains, are eliminated. Also, the FSMCF2NN method, since it is designed based on intelligence methods not only minimizes the accumulation of state errors, but also generates the optimum control efforts of the EMS system to ensure stability in different situations. Furthermore, since the measurement of all EMS states is not achievable in reality, a qualified observer is needed to estimate unmeasured states. For this purpose, the Extended Kalman-Bucy filter (EKBF) is selected because of its superiority in managing disturbances for nonlinear systems. Furthermore, to confirm the robustness of the FSMCF2NN scheme in different scenarios, the results of the controller based on Linear Matrix Inequality (LMI), Linear Quadratic Regulation (LQR) controller, and the conventional Sliding Mode Controller (SMC) are compared with the proposed controller. Analyzing the results of simulation demonstrates that maintaining the levitated object is achievable with the proposed control scheme, effectively. At the same time, it attempts to adequately compensate for the uncertainties and disturbances present in the EMS structure.

A novel open-source cloud control platform with application to tracking control under disturbance

This paper presents a novel rapid control prototyping (RCP) framework for cloud control systems (CCSs). This new architecture consists of human–computer interaction (HCI) client, a cloud server, and an edge controller. It is designed based on open-source hardware and software, with the advantages of low cost, high flexibility, and easy replication. It can offer a reference design for better and further research of CCSs. Then, a cloud-based maglev control system (CMCS) is developed, and a case study on tracking control with disturbance is performed. Furthermore, a dual-structural controller based on equivalent-input-disturbance (EID) and networked predictive control (NPC) approach is devised to enhance the disturbance rejection performance for CCSs with communication constraints. The system is divided into two subsystems, and the separation theorem is applied to simplify the design. A discrete-time EID (DEID) estimator is introduced for one subsystem to compensate for the time delay and the exogenous disturbance. For another subsystem, a time-event-driven strategy-based NPC method is devised to increase the dynamical performance of handling packet loss. Finally, experimental results verified the effectiveness and superiority of the proposed framework and the DEID-based dual-structural control algorithm.

Adaptive Resonant-EIDO-Based Optimized Position Precision Control for Magnetic Levitation System

For improving the multiple disturbance rejection with unknown frequency resonant disturbance, an adaptive resonant equivalent input disturbance observer based optimized position precision control for magnetic levitation system is proposed in this paper. Considering the strong nonlinear character of magnetic levitation system, a model predictive control based optimization solution is designed using taylor series expansion, but the predictive model accuracy is missing. Considering the model error and multiple disturbance, an adaptive resonant equivalent input disturbance estimator is developed, compared with existed results, it could observe the unknown frequency resonant disturbance. Then, the disturbance estimation integrated model predictive control scheme is constructed for online optimization. Rigorous analysis is also given. Compared with existed disturbance attenuation method, experimental result has also shown that the improvement of position precision and disturbance rejection are both confirmed with the proposed control method.

Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System

In this paper, we present a method to design a hybrid fuzzy logic controller (FLC) for a magnetic levitation system (MLS) based on the linear feedforward control method combined with FLC. MLS has many applications in industry, transportation, but the system is strongly nonlinear and unstable at equilibrium. The fast response linear control law ensures that the ball is kept at the desired point, but does not remain stable at that point in the presence of noise or deviation from the desired position. The controller that combines linear feedforward control and FLC is designed to ensure ball stability and increase the system's fast-response when deviating from equilibrium and improve control quality. Simulation results in the presence of noise show that the proposed control law has a fast and stable effect on external noise. The advantages of the proposed controller are shown through the comparison results with conventional PID and FLC control laws.

Adaptive Fixed-Time Position Precision Control for Magnetic Levitation Systems

A novel adaptive fixed-time controller (AFTC) based on disturbance compensation technology is proposed to achieve high performance position precision control for magnetic levitation system in this paper. Firstly, the dynamic model of the magnetic levitation system is established and a fixed-time controller (FTC) is designed to realize the closed-loop control. However, this approach usually requires a large switching gain to suppress interference, resulting in chattering. In view of this, the generalized proportional integral observer (GPIO) is introduced to estimate and compensate the time-varying interference, which can not only improve the anti-interference ability, but also reduce the chattering by choosing a smaller switching gain. Nevertheless, these two performance improvements come at the cost of the dynamic response rate. In order to improve steady state performance without sacrificing dynamic performance, an adaptive fixed-time controller based on GPIO is proposed, which has a significant advantage because of the adjustable switching gain. Specifically, when the system state is far from the sliding mode surface, a larger switching gain is adjusted to improve the convergence rate. When the system state is close to the sliding mode surface, a smaller switching gain is adjusted to reduce chattering. Simulation and experimental results demonstrate the superiority of the proposed AFTC-GPIO method qualitatively and quantitatively. Note to Practitioners–As a highly nonlinear system easily affected by external disturbances and system uncertainty, high precision position control of magnetic levitation system is a great challenge. In this paper, based on the accurate estimation of lumped time-varying interference by GPIO, an adaptive fixed time sliding mode controller is designed to suppress the disturbance and achieve the high precision control. Traditional sliding mode control inevitably has to choose between improving convergence rate and suppressing chattering, and the dynamic and steady performance of the system cannot be considered simultaneously. In view of this issue, this paper combines sliding mode control with adaptive control, and an adaptive and adjustable switching gain is designed, so that the system has the performance of fast convergence and small chattering. Simulation and experimental results verify the effectiveness of the proposed method. The reported AFTC-GPIO idea can also be extended to control of other types of systems.

DEID-Based Control of Networked Rapid Control Prototyping System: Design and Applications

This article presents a novel networked rapid control prototyping (NRCP) system, which can significantly facilitate the theoretical analysis and product design of networked control systems (NCSs). The NRCP system consists of a PC controller equipped with MATLAB/Simulink software and an embedded target designed with open-source hardware, e.g., Raspberry Pi and STM32. Therefore, it benefits from the following advantages, such as simple structure, low cost, convenient deployment, and robust scalability. In order to verify the effectiveness and superiority of the proposed NRCP, a networked magnetic levitation control system (NMLCS) is developed on the basis of this NRCP system. Furthermore, a discrete-time equivalent-input-disturbance (DEID)-based model predictive control approach is proposed for NMLCS to deal with the common practical issues in NCSs, such as time delay and disturbance. Specifically, the time delay is modeled as delay-induced disturbance (DID), whose influence is effectively estimated and eliminated by the designed controller. Several experimental case studies are performed to verify the effectiveness of the NRCP, the DEID-based control algorithm, and the analytical method of DID.

Design of super twisting disturbance observer-based controller for magnetic levitation system

Design of super twisting disturbance observer-based controller for magnetic levitation system

Design of an improved robust fractional order PID controller for magnetic levitation system based on atom search optimization

Design of an improved robust fractional order PID controller for magnetic levitation system based on atom search optimization

Recurrent neural network based high-precision position compensation control of magnetic levitation system

For improving the dynamic quality and steady-state performance, the hybrid controller based on recurrent neural network (RNN) is designed to implement the position control of the magnetic levitation ball system in this study. This hybrid controller consists of a baseline controller, an RNN identifier, and an RNN controller. In the hybrid controller, the baseline controller based on the control law of proportional-integral-derivative is firstly employed to provide the online learning sample and maintain the system stability at the early control phase. Then, the RNN identifier is trained online to learn the accurate inverse model of the controlled object. Next, the RNN controller shared the same structures and parameters with the RNN identifier is applied to add the precise compensation control quantity in real-time. Finally, the effectiveness and advancement of the proposed hybrid control strategy are comprehensively validated by the simulation and experimental tests of tracking step, square, sinusoidal, and trapezoidal signals. The results indicate that the RNN-based hybrid controller can obtain higher precision and faster adjustment than the comparison controllers and has strong anti-interference ability and robustness.

Multisource Oscillation Suppression Control of Magnetic Levitation High Speed Permanent Magnet Motor

The multi-source oscillation of magnetic levitation permanent magnet motor (ML-PMM) under high speed operation is inevitable. The reasons are the influence of the strong coupling characteristics of high speed permanent magnet motor(HSPMM), the nonlinear characteristics of power converter and the coupling characteristics of magnetic levitation control system. Aiming at the multi-source oscillation caused by magnetic levitation system and HSPMM, the multi-source oscillation suppression control method suitable for magnetic levitation-high speed permanent magnet motor(ML-HSPMM) is studied in this paper. The stability of ML-HSPMM and the small signal model of the motor are analyzed. Finally, the control strategy of is verified by simulation and experiment.

Proposal of Sliding Mode Controller based on Backstepping Technique for Control of Magnetic Levitation System

Magnetic Levitation System (MLS) is a nonlinear system and it is used extensively in many areas. The system's goal is to use the non-contact principle to magnetize the coil and cause the objects to float to a specific height. The magnetic force and the current flowing through the coil have a nonlinear relationship. In this paper first design of the backstepping controller is done to attain the wanted floating in presence of system related uncertainties and system behavior is witnessed. Then a Sliding Mode Controller (SMC) based on backstepping procedure is formulated. The control gains in this controller are designed in such a way that the characteristics polynomial whose coefficients are control gains is strictly Hurwitz and the closed loop system's asymptotic stability is assured. Simulations are performed and the magnetic ball tracking is observed considering step signal as reference in presence of disturbances. More accuracy is observed in terms of following the reference by the magnetic ball when it is used Sliding Mode Controller based on Backstepping technique instead of backstepping controller by considering pulse type disturbance. By simulation and analyzing results it is confirmed that the proposed control strategy is more effective.

Gaussian process dynamic modeling and backstepping sliding mode control for magnetic levitation system of maglev train

Gaussian process dynamic modeling and backstepping sliding mode control for magnetic levitation system of maglev train

Comparison of Nonlinear and Linear Controllers for Magnetic Levitation System

Nonlinear system control belongs to advanced control problems important for real plants control design. Various techniques have been developed in this field. In this paper we compare two different approaches to a nonlinear unstable Magnetic levitation system control. The first control design approach further develops our recent results on robust discrete-time pole-placement, based on convex DR-regions. The second studied approach is based on feedback linearization and the simplified development of the corresponding nonlinear control law is provided. Both approaches are compared and evaluated. The efficiency of robust discrete-time pole-placement controller is shown as well as its competitiveness in comparison with nonlinear control for Magnetic levitation system.

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.