State constraints-based finite-time position tracking control for DC motor

A neural networks-based adaptive finite-time position tracking control method for DC motor with state constraints is proposed in this paper. Firstly, the barrier Lyapunov function (BLF) is introduced to constrain the state variables of DC motor, which ensures that the angular position and angular sp...

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Bibliographic Details
Published inNanjing Xinxi Gongcheng Daxue Xuebao Vol. 12; no. 3; pp. 316 - 321
Main Authors Song, Chen, Liu, Jiapeng, Yu, Jinpeng, Lu, Zhenxiang
Format Journal Article
LanguageChinese
Published Nanjing Nanjing University of Information Science & Technology 01.06.2020
Subjects
Adaptive control
Angular position
Angular speed
Control methods
D C motors
Electric motors
Liapunov functions
Neural networks
Tracking control
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Summary:A neural networks-based adaptive finite-time position tracking control method for DC motor with state constraints is proposed in this paper. Firstly, the barrier Lyapunov function (BLF) is introduced to constrain the state variables of DC motor, which ensures that the angular position and angular speed of motor are limited within the given constraint range,the neural networks are used to approximate unknown nonlinear functions in the system. In addition, the finite-time control technology is introduced to improve response speed and convergence speed of the system. The simulation results show that the control method can achieve fast and effective tracking control of DC motor.
ISSN:1674-7070
DOI:10.13878/j.cnki.jnuist.2020.03.009