ARNISMC for MLS with global positioning tracking control

Chen, Seng-Chi; Kuo, Chun-Yi

doi:10.1049/iet-epa.2017.0690

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Nevertheless, little research on this subject has been conducted. Chen and Kuo [14] developed a neural network intelligent slidingmode controller for magnetic levitation systems for realizing the positioning tracking of a steel ball and experiments on the step motion demonstrated its effect. Minihan et al [15] compared the control performances of controllers that are based on fuzzy logic, sliding mode, and direct linearization on tracking the sinusoidal motion of a thrust active magnetic bearing (TAMB).…”

Section: With the Development Of Modern Control Theory Nonlinearmentioning

confidence: 99%

A Dual-Loop Control Approach of Active Magnetic Bearing System for Rotor Tracking Control

2019

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Rotor tracking control, which can be implemented by active magnetic bearing (AMB) system with high precision, can realize many functions, such as attitude control and special surface processing. However, large-motion rotor tracking control is difficult to implemented, due to AMB's highly nonlinear characteristics. In this paper, a dual-loop neural network sliding mode control (DL-NNSMC) system of AMB is proposed for rotor radial tracking control. The complete model of the AMB system is established and the dual-loop control system is designed. A circuit model that considers the rotor motion is established and the model-based inner loop of current control is established, conjointly for dealing with the influence of rotor motion on the current response and the unknown characteristics of the power amplifier. In the outer loop, a nonlinear electromagnetic force model is applied and a wavelet neural network sliding mode control algorithm is designed for accurate position control. Two cases of rotor trajectory tracking are simulated, and the simulation results demonstrate the validity of the proposed control system for large-motion rotor tracking control and its far superior control performance in terms of precision compared with common approaches based on sliding mode control (SMC). INDEX TERMS Active magnetic bearing, rotor tracking control, control system design, AMB system modeling, sliding mode control.

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Section: With the Development Of Modern Control Theory Nonlinearmentioning

confidence: 99%

A Dual-Loop Control Approach of Active Magnetic Bearing System for Rotor Tracking Control

2019

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“…It can directly estimate the unknown nonlinear function without prior information of the closed-loop system 16 , and can improve the position control accuracy of the magnetic levitation ball 17 . Chen et al proposed a sliding mode control method based on radial basis function (RBF) neural network, which significantly improved the control accuracy and robustness of the magnetic levitation ball control system 18 . In response to the problems in neural network training, Patan et al proposed an adaptive iterative learning control method, which greatly improved the convergence speed and stability of neural network controller in maglev control system 19 .…”

Section: Introductionmentioning

confidence: 99%

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Tang

Huang

Zhu

et al. 2022

Sci Rep

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Aiming at the problem of poor transient performance of the control system caused by the control uncertainty of the undertrained neural network, a neural network compensation control method based on fuzzy inference is proposed in this paper. The method includes three control substructures: fuzzy inference block, neural network control block and basic control block. The fuzzy inference block adaptively adjusts the neural network compensation control quantity according to the control error and the error rate of change, and adds a dynamic adjustment factor to ensure the control quality at the initial stage of network learning or at the moment of signal transition. The neural network control block is composed of an identifier and a controller with the same network structure. After the identifier learns the dynamic inverse model of the controlled object online, its training parameters are dynamically copied to the controller for real-time compensation control. The basic control block uses a traditional PID controller to provide online learning samples for the neural network control block. The simulation and experimental results of the position control of the magnetic levitation ball show that the proposed method significantly reduces the overshoot and settling time of the control system without sacrificing the steady-state accuracy of neural network compensation control, and has good transient and steady-state performance and strong robustness simultaneously.

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“…Moreover, for verifying the effectiveness of the proposed controller, some experiments were performed. In another work for the satisfactory tracking performance of the Maglev system, an adaptive recurrent neural network intelligent SMC was designed by Chen and Kuo [26]. Also, by illustrating the validity of the proposed controller, SMC and PID, some experimental results were compared in that paper.…”

Section: Introductionmentioning

confidence: 99%

Control and Robust Stabilization at Unstable Equilibrium by Fractional Controller for Magnetic Levitation Systems

2021

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The problem of control and stabilizing inherently non-linear and unstable magnetic levitation (Maglev) systems with uncertain equilibrium states has been studied. Accordingly, some significant works related to different control approaches have been highlighted to provide robust control and enhance the performance of the Maglev system. This work examines a method to control and stabilize the levitation system in the presence of disturbance and parameter variations to minimize the magnet gap deviation from the equilibrium position. To fulfill the stabilization and disturbance rejection for this non-linear dynamic system, the fractional order PID, fractional order sliding mode, and fractional order Fuzzy control approaches are conducted. In order to design the suitable control outlines based on fractional order controllers, a tuning hybrid method of GWO–PSO algorithms is applied by using the different performance criteria as Integrated Absolute Error (IAE), Integrated Time Weighted Absolute Error (ITAE), Integrated Squared Error (ISE), and Integrated Time Weighted Squared Error (ITSE). In general, these objectives are used by targeting the best tuning of specified control parameters. Finally, the simulation results are presented to determine which fractional controllers demonstrate better control performance, achieve fast and robust stability of the closed-loop system, and provide excellent disturbance suppression effect under nonlinear and uncertainty existing in the processing system.

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ARNISMC for MLS with global positioning tracking control

Cited by 8 publications

References 14 publications

A Dual-Loop Control Approach of Active Magnetic Bearing System for Rotor Tracking Control

A Dual-Loop Control Approach of Active Magnetic Bearing System for Rotor Tracking Control

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

Control and Robust Stabilization at Unstable Equilibrium by Fractional Controller for Magnetic Levitation Systems

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