Nonlinear Position Stabilizing Control with Active Damping Injection Technique for Magnetic Levitation Systems

Kim, Seok-Kyoon

doi:10.3390/electronics8020221

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…From the nonlinear dynamic equation of the magnetic levitation system of expression (12), the T-S fuzzy model of the system is derived by the method of ''sector nonlinearity''. For equation (12), define two nonlinear terms,…”

Section: Integral T-s Fuzzy Controlmentioning

confidence: 99%

“…The principles of the SMC and the approximation capability of the fuzzy systems are utilized to compensate for the adaptive approximation error, and the neural-fuzzy switching law is employed to reduce the chattering. In [12], a novel nonlinear position-stabilizing controller for magnetic levitation is proposed by combining the active damping injection technique and disturbance observers (DOBs). In [13], in view of the wide application of the internet of things (IoT) in intelligent transportation, a new method for realizing suspension control for mediumlow-speed maglev trains using the IoT and an adaptive fuzzy controller is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Design and Real-Time Implementation of Takagi–Sugeno Fuzzy Controller for Magnetic Levitation Ball System

2020

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An integral state feedback control method based on T-S fuzzy model is proposed for nonlinear and unstable magnetic levitation ball system in this paper. Firstly, the fuzzy model of the magnetic levitation ball is derived from the nonlinear dynamic model by using the sector nonlinearity, and the local controller is designed by using the integral state feedback control. The global controller is constructed by a parallel distributed compensation (PDC) method, and the feedback gain is obtained using a linear matrix inequality (LMI). Finally, the integrated state feedback controller is applied to the position control of the magnetic levitation ball system, and a dSPACE real-time control platform is established for experimental research. The simulation and experiment are performed to prove that the designed controller can levitate the ball stably, and has better control performance. INDEX TERMS Magnetic levitation ball system, T-S fuzzy model, integral state feedback, parallel distributed compensation (PDC), linear matrix inequality (LMI), dSPACE.

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Section: Integral T-s Fuzzy Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design and Real-Time Implementation of Takagi–Sugeno Fuzzy Controller for Magnetic Levitation Ball System

2020

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“…The combination of simple proportional-type control was presented through a back-stepping process, forming a multi-loop structure [28]. Active damping-based multi-loop controllers incorporating DOBs solved the system parameter dependence problem [29]. The elimination of the velocity sensor was accomplished using a recent DOBbased proportional-derivative (PD) controller, including the parameter-independent velocity observer [30] with convergence and closed-loop behavior analysis.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Positioning Technique via Dynamic Current Cut-Off Frequency and Observer-Based Pole-Zero Cancellation Approaches for MAGLEV Applications

et al. 2022

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This article solves the problem caused by high level current feedback gain setting for fast responsiveness of magnetic levitation systems considering the current dynamics and presents advanced nonlinear positioning technology without plant parameter information. The main features of this study are summarized as follows: First, current control demonstrates current ripple reduction and overall performance guarantee through a low feedback gain in the steady state, including a dynamic feedback loop increased by an error variable magnitude in the transient period. Second, the plant parameter information-free velocity observer replaces the observer output error integral action with the disturbance estimation action to improve the closed-loop performance. The simulation results reveal the practical advantages derived from the contributions of this study.INDEX TERMS Magnetic levitation, positioning, variable cut-off frequency, velocity observer, disturbance observer. NOMENCLATURE PLANT VARIABLES M , M LMasses of electromagnet and load. g, K Gravitational acceleration and electromagnetic force coefficient. R c , L cCoil resistance and inductance. M 0 , K 0 , R c,0 , L c,0 Norminal parameter values. wp , wcThe uncertain time varying disturbances.The associate editor coordinating the review of this manuscript and approving it for publication was Philip Pong . p, v p , i c , V Position, velocity, current, voltage of MAGLEV system. p * , i * c Desired position and coil current. p ref , i c,ref Reference position and coil current. ω c , ω p Cut-off frequencies. CONTROLLER VARIABLES e p , e v Estimation errors of position and velocity. d p , dp , d c , dc Lumped disturbance and disturbance estimation. l v,i Design parameters of velocity observer. z ref Control input variable. p p ref − p error.

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“…The demand for a compact and convenient, environment-friendly, reduced maintenance of public transportation is rising [1]. The body position control in magnetic levitation systems takes place through an electromagnetic force [2]. The Magnetic Levitation (Maglev) based train is an efficient and frictionless transport system that fulfills such demands.…”

Section: Introductionmentioning

confidence: 99%

A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

2021

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Maglev transportation system is become a hot topic for researchers because of the distinctive advantages, such as frictionless motion, low power consumption, less noise, and being environmentally friendly. The maglev transportation system’s performance gets sufficiently influenced by the control method and the magnetic levitation system’s dynamic performance, which is a critical component of the maglev transportation system. The Magnetic Levitation System (MLS) is a group of unstable, nonlinear, uncertain, and electromagnetically coupled practical application. Control objective of this study is to design a position stabilizing control strategy for Magnetic Levitation system under extreme uncertain parametric conditions using a reference model governed by a reference stabilizer and nonlinear adaptive control structure. After successful tuning the reference stabilizer with and without time-varying payload disturbance, the tracking-error dynamics are obtained in the presence of both matched and mismatched types of parametric uncertainties. Next, the close-loop stability theorem is formulated for Lyapunov stability analysis to get the design constraints, parameter update laws, and adaptive control law. Numerical simulations performed for a high range of parametric violations check the control design’s efficacy. The performance robustness gets confirmed by comparing the results with the nonlinear control approach. The MLS gets performance recovery and settles within safe limits in few seconds using the proposed methodology. However, the nonlinear controller faces permanent failure in stabilizing the MLS.

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Nonlinear Position Stabilizing Control with Active Damping Injection Technique for Magnetic Levitation Systems

Cited by 8 publications

References 23 publications

Design and Real-Time Implementation of Takagi–Sugeno Fuzzy Controller for Magnetic Levitation Ball System

Design and Real-Time Implementation of Takagi–Sugeno Fuzzy Controller for Magnetic Levitation Ball System

Nonlinear Positioning Technique via Dynamic Current Cut-Off Frequency and Observer-Based Pole-Zero Cancellation Approaches for MAGLEV Applications

A Reference Model Assisted Adaptive Control Structure for Maglev Transportation System

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