Uncertainty and disturbance estimator-assisted control of a two-axis active magnetic bearing

Kuperman, Alon

doi:10.1177/0142331216634426

Cited by 14 publications

(5 citation statements)

References 56 publications

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“…In the paper 'Uncertainty and disturbance estimator assisted control of a two-axis active magnetic bearing', Kuperman (2016) presents a robust non-linear control strategy for a two-axis active magnetic bearing position stabilisation. The approach is based on the feedback lin-earisation method and the uncertainty and disturbance estimator.…”

Section: Application To Mechatronic Systemsmentioning

confidence: 99%

Disturbance observers and applications

Yang

Chen

Ding

2016

Transactions of the Institute of Measurement and Control

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Section: Application To Mechatronic Systemsmentioning

confidence: 99%

Disturbance observers and applications

Yang

Chen

Ding

2016

Transactions of the Institute of Measurement and Control

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“…Issues such as the identification and development of internal model control in order to reduce bias and harmonic currents (Xu et al, 2019), position stabilization of a two-axis active magnetic bearing using robust feedback linearization method (Kuperman, 2016), the development of an adaptive fuzzy neural position control algorithm (Sun et al, 2019) and extending a modelling approach for compensating the non-contact inductive gap sensor of the high-speed maglev train using RBF neural network (Jing et al, 2013) are among the studies conducted in this field.…”

Section: Introductionmentioning

confidence: 99%

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

Fatemimoghadam

Toshani

Manthouri

2020

Transactions of the Institute of Measurement and Control

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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.

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“…It is suitable for controlling such an unstable, fast-response maglev system [5]. A robust nonlinear control strategy is developed for a class of second-order nonlinear uncertain systems with uncertainties and disturbances and is applied to two-axis active magnetic bearing position stabilization, It calculates and robustly cancels system uncertainties and disturbances via appropriate filtering [6]. A new criterion for selecting the weighting matrices of LQR is proposed, the experimental results prove that the proposed control strategy is effective in stabilizing the ball [7].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Rejection Control Using a Novel Velocity Fusion Estimation Method for Levitation Control Systems

Xia

Dou

2020

IEEE Access

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Magnetic levitation has been applied to maglev trains and magnetic suspension wind tunnel. However, there are some problems with the existing levitation control. For example, it is difficult to extract smooth velocity signals from the gap sensor with noise. The classical differentiator is susceptible to noise, which makes the levitation system sometimes vibrate. The accuracy and stability of levitation control need to be improved. The velocity fusion estimation method (VFE) is proposed to extract the velocity signal from the gap and acceleration sensor, which is theoretically derived to prove that it reduced the noise of the velocity signal. Then disturbance rejection control(DRC) is proposed that add VFE into classical levitation control. Because of the high-quality velocity signal and accelerometer's fast responsiveness, it makes DRC have many advantages. The advantages of using the proposed control structure are that it improved the control accuracy of the target gap and resisted disturbance effectively. Air-gap fluctuations in levitation systems can be reduced in transient pulse disturbance test, white noise disturbance test, and external force disturbance test when it applies the proposed control method. The effectiveness and advantages of the proposed control method are verified by the simulation and experiments.

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Uncertainty and disturbance estimator-assisted control of a two-axis active magnetic bearing

Cited by 14 publications

References 56 publications

Disturbance observers and applications

Disturbance observers and applications

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

Disturbance Rejection Control Using a Novel Velocity Fusion Estimation Method for Levitation Control Systems

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