Perturbation wavelet neural sliding mode position control for a voice coil motor driver

Hsu, Chun‐Fei; Kao, Wei-Fu

doi:10.1007/s00521-018-3413-5

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…Professor Li Liyi of Harbin Institute of Technology [1] developed a compound control strategy based on "PID+single neuron control" with self-learning and self-adapting capabilities and enhanced the dynamics of the system response by adding a feedforward compensator to the position loop control loop. According to the nonlinear characteristics of voice coil motors, Chun-Fei Hsu [2] of Tamkang University in Taiwan proposed a Perturbation Wavelet Neural Sliding Mode Position Control (PWSPC) system for motor drivers.…”

Section: Prefacementioning

confidence: 99%

Application of Fuzzy Sliding Mode Control in Voice Coil Motor Control System

Zhao

Liu

Jia

et al. 2022

J. Phys.: Conf. Ser.

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The requirements of modern industry on the response speed and accuracy of voice coil motors has been the gradual growth, and PID control algorithms have become increasingly unable to meet their requirements. The use of sliding mode control can speed up the response time of the motor, and it also can improve the response speed, overshoot and instability problems in some degree. However, the sliding mode control algorithm will bring jitter to the entire system. This paper combines fuzzy control and sliding mode control to adjust the parameters of the sliding mode control algorithm in real time, which greatly reduces the jitter problem caused by the sliding mode. According to the actual parameters of the voice coil motor, the transfer function of the voice coil motor is deduced, a simulation model of fuzzy sliding mode control is built, and the sliding mode control and fuzzy sliding mode control algorithms are compared. Finally, two signals of step and sine are used as inputs for simulation model. It can be seen through the simulation waveform that the fuzzy sliding mode control algorithm can improve the response speed of the control system and reduce the tracking error.

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Section: Prefacementioning

confidence: 99%

Application of Fuzzy Sliding Mode Control in Voice Coil Motor Control System

Zhao

Liu

Jia

et al. 2022

J. Phys.: Conf. Ser.

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“…applied. Recent research on RBF neural network adaptive control has attracted considerable attention [7]- [15], and a method for analyzing the stability of neural network adaptive control based on the Lyapunov method reported elsewhere [16]- [18] has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Fixed-Time Adaptive Neural Tracking Control for a Class of Uncertain Nonlinear Pure-Feedback Systems

Cheng

Dai

et al. 2020

IEEE Access

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This paper presents fixed-time adaptive neural tracking control for a class of uncertain nonlinear pure-feedback systems. To overcome the design difficulty arising from the nonaffine structure of nonlinear pure-feedback systems, the mean value theorem is introduced to separate the nonaffine appearance of nonlinear pure-feedback systems. Radial basis function (RBF) neural networks are employed to approximate designed unknown functionsf i (Z i). By combining RBFs and Lyapunov functions, a novel fixed-time controller is designed, and semiglobal uniform ultimate boundedness of all signals in the closed-loop control system is guaranteed in a fixed time. Sufficient conditions are given to ensure that the system has semiglobal fixed-time stability. The main purpose of this paper is to design a controller for an unknown nonlinear purefeedback system so that the system output y can track the reference signal y d. The simulation experiments indicate that the selection of sufficient design parameters makes the tracking error converge on a domain of the origin. Compared with the existing finite-time control and fixed-time control, the proposed fixed-time control scheme reduces the size of the tracking error. INDEX TERMS Adaptive neural network, fixed-time control, nonlinear pure-feedback systems.

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A TOPSIS multi-criteria decision method-based intelligent recurrent wavelet CMAC control system design for MIMO uncertain nonlinear systems

Lin

2018

Neural Comput & Applic

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Perturbation wavelet neural sliding mode position control for a voice coil motor driver

Cited by 8 publications

References 28 publications

Application of Fuzzy Sliding Mode Control in Voice Coil Motor Control System

Application of Fuzzy Sliding Mode Control in Voice Coil Motor Control System

Fixed-Time Adaptive Neural Tracking Control for a Class of Uncertain Nonlinear Pure-Feedback Systems

A TOPSIS multi-criteria decision method-based intelligent recurrent wavelet CMAC control system design for MIMO uncertain nonlinear systems

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