Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

Yao, Xuan; Chen, Zhaobo

doi:10.1177/0142331218778324

Cited by 25 publications

(20 citation statements)

References 29 publications

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“…Developing an adaptive backstepping control law where a recurrent neural network (RNN) has been used to estimate the uncertainty in real-time (Lin et al, 2009), designing an adaptive fuzzy backstepping control technique where the fuzzy system has been used for estimating the nonlinear input function, and an adaptive law has been derived to guarantee the closed-loop stability (Sadek et al, 2017) and using an adaptive backstepping control technique in the presence of parametric uncertainty (Zhou and Liu, 2013) are some researches in this area. In addition, several nonlinear control techniques have been developed for compensating magnetic bearing systems (Bobtsov et al, 2018; Yao and Chen, 2019) and maglev system (Wai and Chuang, 2010; Wai et al, 2014).…”

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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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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“…Since the AMB system provides a contactless suspension to yaw gimbal, the vibration disturbances from other gimbals could be effectively isolated, and the friction between levitated gimbal and suspension elements would be minimized [18]. Moreover, the displacement of yaw gimbal is controllable by regulating the winding current of AMB system based on displacement feedback [19][20][21][22][23], and then the vibration isolation could be realized with the active controllability of an AMB system [24].…”

Section: Introductionmentioning

confidence: 99%

Coupling Analysis and Cross-Feedback Control of Three-Axis Inertially Stabilized Platform with an Active Magnetic Bearing System

Tong

Xiang

Wong

2020

Shock and Vibration

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An active magnetic bearing (AMB) system is used to suspend the yaw gimbal of three-axis inertially stabilized platform (ISP) to minimize the friction. The dynamic functions of three gimbals in ISP are developed. The base coupling at dynamic base plate is stronger than that at static base plate, and the gimbal coupling among three gimbals increases with the number of unlocked gimbals. Therefore, a cross-feedback control scheme is designed to minimize the base coupling and the gimbal coupling, and then the multi-input multioutput system of three-axis ISP with coupling terms is simplified into three decoupled single-input single-output systems. Experimental results verify that the yaw gimbal suspended by AMB system has better vibration isolation ability than the roll gimbal supported by mechanical bearing, and the gimbal coupling and the base coupling are effectively suppressed by the cross-feedback control scheme.

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“…e issue of SMC for stochastic semi-Markovian jump systems was studied in [6,7] with application to space robot manipulators. A sliding mode controller of active magnetic bearing was proposed in [8] to achieve rotor 3D trajectory control. A constraint design with a sliding mode strategy was proposed in [9] to improve the stability of aircraft engine control.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Sliding Mode Control of a VAV Air-Conditioning Terminal Temperature System

Niu

Yang

et al. 2020

Complexity

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A varied air volume (VAV) air-conditioning system comprises diverse input and/or output disturbances, which are commonly nonlinear, with large lag and uncertainty. Based on the traditional control methods, testing the controlling parameters of a VAV air-conditioning system is challenging. Sliding mode control could improve the robustness of the system due to the adaptive capacity of disturbance rejection. Moreover, the fuzzy algorithm could be employed to determine the stability of a sliding control system by adjusting the parameters in the approach rate, reducing the switching frequency, and weakening chatter. Fuzzy sliding mode control is investigated in this paper to improve the performance of a VAV air-conditioning system. Simulation results verify that the sliding mode control is suitable for a VAV air-conditioning system to achieve good adaptability and robustness considering the influence of multiple external variable disturbances. In addition, the chattering of the system is improved after the introduction of fuzzy control.

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Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

Cited by 25 publications

References 29 publications

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

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

Coupling Analysis and Cross-Feedback Control of Three-Axis Inertially Stabilized Platform with an Active Magnetic Bearing System

Fuzzy Sliding Mode Control of a VAV Air-Conditioning Terminal Temperature System

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