Levitation Control Research Articles

Analysis of dynamic levitation process of the particle chain in a nonlinear standing wave field.

This research delves into the dynamic behavior of acoustic levitation of the particle chain in a nonlinear standing wave field. Experimental acoustic levitation control tests reveal bifurcation and jump phenomena during dynamic adjustments to resonant cavity height. Employing the 10-particle chain experiments and the COMSOL simulation models, the Sine-Gordon 2D vibration model is established to study the dynamic deformation process of the particle chain. The study uncovers the nonlinear interaction of particle lateral vibrations, horizontal acoustic radiation force, and conical wave fields that generate the jumping standing wave field. Notably, the fourth particle acts as a prominent jumping critical point in the secondary standing wave field, facilitating the derivation of the particle chain's nonlinear levitation dynamics. This discovery provides us with a new method to regulate the particle chain system.

Linear Model Predictive Control and Back-Propagation Controller for Single-Point Magnetic Levitation with Different Gap Levitation and Back-Propagation Offline Iteration

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved.

Double-Sided Single-Set Winding Coordinated Active Levitation Control of Linear Permanent Magnet Motor Considering Mover Air-Gap Offset

Double-Sided Single-Set Winding Coordinated Active Levitation Control of Linear Permanent Magnet Motor Considering Mover Air-Gap Offset

Dynamic impact analysis of the time-delay levitation control system on maglev vehicle system after adding smith predictor

The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms.

Stochastic optimization of levitation control system for maglev vehicles subjected to random guideway irregularity

The dynamic performance of maglev vehicle-bridge coupled systems subjected to random guideway irregularity, especially the levitation stability of the running vehicle subsystem, is a critical issue affecting the safe operation and the comfort riding of maglev railways. This study is devoted to stochastic optimization of the levitation control system considering the coupled vibration of maglev vehicle and bridge under the random guideway irregularity. To alleviate the computational burden in the stochastic optimization process, the explicit expressions of the critical responses of the maglev vehicle-bridge coupled systems and the sensitivities of critical responses with respect to the feedback gains of the proportional-integral-derivative (PID) levitation controller are respectively constructed in terms of the guideway irregularity by virtue of the dimension-reduced formulation capability of the explicit time-domain method (ETDM), enabling stochastic dynamic analysis and stochastic sensitivity analysis to be executed at high efficiency. The stochastic optimization problem is formulated as the minimization of the standard deviation of the air gap variation with the constraint on the standard deviation of the current variation, and the optimal values of the PID gains are searched by the gradient-based method of moving asymptotes (MMA), in which the statistical moments and moment sensitivities of the critical responses required in the optimization process are obtained by ETDM. A numerical example with a 2-degree-of-freedom maglev vehicle moving on a multi-span simply-supported bridge is used to demonstrate the efficacy of ETDM for the stochastic analysis of maglev vehicle-bridge coupled systems under guideway irregularity. An engineering example involving a 3-car maglev train traversing a multi-span simply-supported bridge is further presented to show the effectiveness of the ETDM-based approach for the stochastic optimization of large-scale engineering problems. The levitation stability of the maglev train under the action of random guideway irregularity is considerably improved with the use of optimal feedback gains.

Research on Magnetic Levitation Control Method under Elastic Track Conditions Based on Backstepping Method

The vehicle–guideway coupled self-excited vibration of maglev systems is a common control instability problem in maglev traffic while the train is suspended above flexible girders, and it seriously affects the suspension stability of maglev vehicles. In order to solve this problem, a nonlinear dynamic model of a single-point maglev system with elastic track is established in this paper, and a new and more stable adaptive backstepping control method combined with magnetic flux feedback is designed. In order to verify the control effect of the designed control method, a maglev vehicle–guideway coupled experimental platform with elastic track is built, and experimental verifications under rigid and elastic conditions are carried out. The results show that, compared with the state feedback controller based on the feedback linearization controller, the adaptive backstepping control law proposed in this paper can achieve stable suspension under extremely low track stiffness, and that it shows good stability and anti-interference abilities under elastic conditions. This work has an important meaning regarding its potential to benefit the advancement of commercial maglev lines, which may significantly enhance the stability of the maglev system and reduce the cost of guideway construction.

Finite-time disturbance observer-based levitation control for vehicle-guideway coupling systems

In this study, a novel composite control scheme for the vehicle-guideway coupling systems is proposed, consisting of FTDOs and a FTC, aiming to address the challenges of unknown disturbances and vibration suppression. Specifically, this method adopts a single magnet-track coupling model and introduces a finite-time disturbance observer (FTDO) that utilizes only measured electromagnet-side signals to estimate unmeasurable states and unknown disturbances. Based on the estimated information provided by the FTDO, a finite-time control (FTC) scheme is developed, which simultaneously handles the problems of disturbance compensation and finite-time tracking control. Additionally, the finite-time stability of the levitation system is analyzed and proven. Finally, simulation and experimental results are given to demonstrate the feasibility and superiority of the proposed control approach.

Ultralocal Model-Free Adaptive Supertwisting Nonsingular Terminal Sliding Mode Control for Magnetic Levitation System

The magnetic levitation technology has attracted blooming attention from all walks of life thanks to its superiorities such as non-contact and active control. While the intrinsic features of open-loop instability and strong nonlinearity, along with the uncertain external disturbance make the levitation control particularly significant and difficult. For speeding up the convergence and obtaining a better stability and disturbance robustness, this paper proposes an ultra-local model-free adaptive super-twisting nonsingular terminal sliding mode control (ULMF-ASTNTSMC) strategy for the magnetic levitation system. Different from the conventional model-based control, ultra-local model-free control breaks the constraint of a precise system model. Therefore, an improved second-order ultra-local model is established for the magnetic levitation system. For the benefit of finite time convergence and non-singularity, the nonsingular terminal sliding mode surface is chosen. As a second-order algorithm, super-twisting (ST) algorithm can fundamentally suppress the chattering effect of the sliding mode control (SMC). An adaptive super-twisting algorithm is designed for the further improvement of the system dynamic response. The stability of the proposed scheme is strictly verified based on the Lyapunov stability theory. Simulation and experimental comparisons with the existing control methods validate the exceeding performance of the proposed strategy under various operating conditions.

Model Predictive Control of Magnetic Helical Swimmers in Two-Dimensional Plane

Magnetic micro/mini-swimmers have a great potential application in biomedical research and have gained broad attention. Recent research studies focus on the automatic control methods of magnetic micro/mini-swimmers, such as artificial intelligence methods. However, their autonomous manipulation remains a challenge since they are subject to various disturbances from the external environment and model uncertainties. The current methods employ a status observer to estimate these disturbances and uncertainties. In this paper, we apply a data-driven technique that utilizes the nonlinear approximation ability of neural networks (NNs). To be specific, a flexible structure of NNs, Broad Learning System (BLS), is employed to model the input-output mapping relationship between the direction of the rotating magnetic field and the swimming direction of the helical miniature swimmer. Then, according to the dual-variable decoupling control, a levitation controller is formulated and a path following controller is proposed based on a planar three-degree-of-freedom model of magnetic helical swimmers, which is inspired by the model of wheeled mobile robots. Simulations and experiments are conducted to quantitatively validate the proposed control method using different planar paths. The experiment results show that the mean absolute error of the path following control is about 3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\%$</tex-math> </inline-formula> of the body length which is less than 0.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$mm$</tex-math> </inline-formula> . Our proposed control method provides a preliminary study to alleviate the impact of disturbances and uncertainties on the control performance of magnetic micro/mini-swimmers. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is mainly motivated by the potential application of artificial intelligence methods in magnetic micro/mini-robot community, especially for the applications that reject disturbances and uncertainties. In practice, a path planner is employed to compute a pre-defined reference path that connects the start and the targeted locations. Then our control method is applied to the magnetic helical swimmer and it is guided to follow the reference path. Tackling external disturbances and model uncertainties is still challenging during the control progress. Neural network-based technique is an intuitive approach since their nonlinear modeling ability and generalization are suitable for estimating these disturbances and uncertainties. As for training the neural-network model, the training data about the angle parameters should be recorded by manual control of the helical swimmer. These angle parameters define the rotating axis of the uniform rotating magnetic field and the self-rotating axis of the magnetic helical swimmer. According to the planar motion model, the optimal controller is formulated using the feedback distance error and angle error, and the sum of the control signal and the prediction of the compensating model is used as the final control input. Our electromagnetic coil system features easy operation and configuration of cameras or other sensors. Simulations and experiments validate the performance of the neural network-based compensating method and the proposed optimal control method using magnetic micro/mini-swimmers.

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.

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains. Designing a controller for the levitation system is challenging due to the strong nonlinearity, open-loop instability, and the need for fast response and security. In this paper, we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control (DO-TMPLC) scheme combined with a feedback linearization strategy for the levitation system. The proposed strategy incorporates state constraints and control input constraints, i.e., the air gap, the vertical velocity, and the current applied to the coil. A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system. Then, a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances. Furthermore, we analyze the recursive feasibility and input-to-state stability of the closed-loop system. The simulation results indicate the efficacy of the proposed control strategy.

Magnetic Levitation Control of Doubly-Fed Linear Motor with Multi-Phase AC Excited Mover

Magnetic Levitation Control of Doubly-Fed Linear Motor with Multi-Phase AC Excited Mover

Decoupled Topologies and Radial Levitation Control of a Novel Permanent Magnet Biased Bearingless Switched Reluctance Motor

Decoupled Topologies and Radial Levitation Control of a Novel Permanent Magnet Biased Bearingless Switched Reluctance Motor

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.

Mathematical modeling of robust automatic control system of magnetic levitation with uncertain parameters

The topic of the research is the development of automated control systems for real processes, which must satisfy various requirements, for example, resistance to unmodeled dynamics (robust stability), as well as meet the required quality of transient processes that arise in the case of external disturbances of various natures. In this regard, there is a need to create appropriate mathematical models for the control of non-stationary objects, the model of which includes uncertain parameters. The problem of developing and studying a mathematical model for controlling the process of electrodynamic magnetic levitation, the main problem of which is dynamic stability, is considered. It is noted that systems using the effect of magnetic levitation are widely used, for example, in shipbuilding, in elements of ship mechanisms, instrument making, as well as in the transportation of various cargoes. The relevance of the work related to the need to develop such automatic control systems that can suppress the emerging oscillatory motion of levitating bodies is substantiated. The initial mathematical model of the magnetic levitation control process, which has uncertain coefficients in differential equations and is nonlinear, is considered. To “hang” at a given (working) point of a levitating body, it is enough to create a mathematical control model in the vicinity of this point based on the linearization of the original mathematical model. A PID-based controller and feedback are added to the resulting model. The four controller coefficients are adjusted using special algorithms, taking into account the requirements for the robustness of the control system. Numerical experiments are carried out to analyze the behavior of the control system depending on the magnitude of the parameter uncertainty. Based on the analysis performed, a conclusion about the robustness of the developed control system for the object under consideration is made. The results of the study are presented in graphical form. The MATLAB system is used as a toolkit.

Study on levitation control performance assessment of maglev train based on minimum entropy

In order to meet the strict requirements of the reliability and safety of the commercial operation of the maglev train, it is necessary to carry out the control performance evaluation of the suspension system. However, at present, variance is often used as the evaluation index of control system. This method cannot get accurate performance evaluation results when the system is affected by non-Gaussian disturbance. Therefore, aiming at the non-Gaussian control performance evaluation of maglev train suspension system, a control performance evaluation method of maglev train suspension system based on minimum entropy is proposed in this paper. In view of the non-Gaussian characteristics in the operation data of maglev train suspension system, this paper introduces the feedback invariant entropy of non-Gaussian system, and then realizes the evaluation and analysis of the control performance of maglev train suspension system based on the minimum entropy criterion. Finally, combined with the evaluation and analysis of the operation data of Changsha medium and low-speed maglev train, this paper verifies the effectiveness and accuracy of the proposed performance evaluation method for the control performance evaluation of maglev train suspension system.

Collaborative control of a levitation module for maglev trains with physical contact prevention and user-defined convergence time

The levitation module of maglev trains is contact-prone between electromagnets and guideways due to its high nonlinearity and various external disturbances. Hence, the coupling structure between the electromagnetic points in a levitation module calls for safe and collaborative control. To achieve this goal, this study proposes a novel collaborative levitation control scheme with physical contact prevention. Considering operational safety requirements confronted by the levitation system, a barrier function is introduced to address the physical contact problem, which strictly limits the levitation gap within an allowable range. Moreover, considering the transient response of the controlled system, the predefined-time stability is synthesised, which ensures that the tracking errors converge to a very small neighbourhood of zero within an arbitrarily settling time determined by users. This brings great convenience of tuning controller parameters and avoids non-trivial tuning with a large set of parameters by trails and errors. In addition, quadratic functions are integrated to guarantee the nonsingularity of control signals to enhance practicality. A series of numerical simulations verify the effectiveness of the proposed control scheme.

Motion Control of Capsule Robot Based on Adaptive Magnetic Levitation Using Electromagnetic Coil

In view of the magnetically actuated capsule robot applications in diagnoses of human stomach disease, the challenges that are associated with degrees-of-freedom (DOF), environmental adaptability, and the size of the entire system must be addressed. In this study, a new electromagnetic coil system that is based on adaptive magnetic levitation is presented; it is mainly composed of an independent orientation control electromagnetic coil and a magnetic levitation control electromagnetic coil. The system is designed to perform motion control of the capsule, including 3-DOF levitating translational motion control, and pitch and yaw motion control in levitation. In this manner, it compensates for the lack of previous magnetic levitation systems that were based on electromagnetic coils for the control of the tilt angle of the capsule. With torque-based actuation control, the tilt angle of the capsule can be controlled continuously with an angle increment of no more than 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> (within 30 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{\circ}$</tex-math> </inline-formula> ) during levitation. The capsule under the magnetic levitation control, which is based on the fuzzy proportional-integral -derivative controller, can maintain the stability of levitation; the error between the actual capsule position and the required position did not exceed 0.1 mm when the capsule was out of water or completely immersed in it. Moreover, the experiment of the capsule levitating from air to water further verifies the adaptability of the system with regard to the environment; this provides a new examination method for capsule endoscopy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is caused by the motion manipulation problem of a magnetically driven capsule robot. Most of the existing magnetically driven capsule robots use a rolling or sliding method that fits tightly against the stomach wall, and these forms of motion make it possible to risk missing diagnoses in the examination of gastric diseases, and the maneuverability limits its further application. This paper proposes an adaptive control strategy for a magnetically driven capsule robot based on magnetic levitation motion. It allows the magnetically driven capsule robot to levitate to any position in the stomach for photography and observation without external mechanical support, and this levitation motion is sufficiently stable. In addition, the proposed capsule robot has independent orientation and position control, which has the potential to achieve automated inspection.

Development of a Conveyor Cart with Magnetic Levitation Mechanism Based on Multi Control Strategies

This paper presents the experimental magnetic levitation control development of Sanki Engineering airport luggage conveyor carts which have four magnetic levitation units working synchronously. With the PID controller, the state feedback controller and the zero-power controller refined by PID controller were implemented in the one magnetic levitation unit system and four-unit magnetic levitation system, and the displacement and the current were verified in a real-time system. The magnetic levitation unit had a fast response, and the control algorithms were easily implemented. The change of current and displacement were compared. In the one-unit system, the PID and state feedback controller react to the disturbance at the same speed and have similar power consumptions. For a disturbance on the zero-power controller, the system generates a transient current to deal with the load disturbance and finally settles to 0 A. The PID control for four magnetic levitation units of the conveyor cart has a better stable performance during synchronous operation. Under the control of state feedback controller, they can keep the cart statically stable with some oscillation. These characteristics are experimentally confirmed.

A Novel Wrench–Current Decoupling Strategy to Extend the Use of Small Lookup Data for a Long-Range Maglev Planar Motor

The maglev planar motor is one of the most promising industrial applications. The planar motor can increase flexibility in modern manufacturing with the multidirectional motion of the mover. In levitation control, the decoupling matrix is used to decouple the strong cross-coupling effect. The Lorentz force-based wrench matrices can be precomputed and stored in the lookup table. However, the motion range is restricted by the data range. This paper presents a wrench–current decoupling strategy to extend the use of small lookup data for long-range planar motion. The horizontal data range is 40 mm by 40 mm, which is determined from the minimally repetitive area of the planar coil array. The quadrant symmetry transformation is used to estimate the data for other areas. The experiment results demonstrated the accomplishment of the developed technique for long-range motion with a maximum motion stroke of 380 mm. The disc-magnet movers can levitate with a large air gap of 30 mm and have a total roll and pitch rotation range of 20 degrees.