Guang Yang
ABSTRACT
Electromechanical actuators are currently widely used in various engineering fields, whose advantages include convenient maintenance and strong stability. To meet the requirements of control precision and response speed for electromechanical actuators, a non-singular fast terminal sliding mode control method based on fuzzy neural network was designed in this paper. The non-singular fast terminal sliding mode controller can not only speed up the response tracking speed, but also solve the singular phenomenon that the fast terminal sliding mode control will make the input of the control system show an infinite trend to let the system will eventually stabilize. Aiming at the nonlinear factors existing in the system, the universal approximation characteristics of the fuzzy Radial Basis Function (RBF) neural network were used to track and compensate for nonlinear factors. The Lyapunov stability theorem was adopted to prove the stability of the designed controller. Through the repeated correspondence of simulation and experiment, it was proved that the designed controller has better control precision than the currently commonly used proportional-integral-derivative (PID) controller and Adaptive Robust Controlller (ARC).
- Wang Y, Wu C, Li J, Event‐triggered switching‐dependent integral sliding mode control for networked switched linear systems with unknown nonlinear disturbance[J]. International Journal of Robust and Nonlinear Control, 2022 (10):32.Google Scholar
- Ghosh S K, Roy T K, Pramanik M, Design of Nonlinear Backstepping Double-Integral Sliding Mode Controllers to Stabilize the DC-Bus Voltage for DC–DC Converters Feeding CPLs[J]. Energies, 2021, 14.Google Scholar
- Zirkohi M M. Fast terminal sliding mode control design for position control of induction motors using adaptive quantum neural networks[J]. Applied Soft Computing, 2022(115-).Google Scholar
- Moafi S A, Najafi F. Fuzzy impedance-based control with fast terminal sliding mode force control loop for a series elastic actuator system[J]. Transactions of the Institute of Measurement and Control, 2022, 44(4):905-915.Google ScholarCross Ref
- Yong F, Yu X, Man Z. Non-singular terminal sliding mode control of rigid manipulators[J]. Automatica, 2002, 38(12):2159-2167.Google ScholarDigital Library
- Su H, Luo R, Fu J, Fixed Time Control and Synchronization for Perturbed Chaotic System Via Nonsingular Terminal Sliding Mode Method[J]. Journal of Computational and Nonlinear Dynamics, 2021 (3):16.Google Scholar
- Hao L, Dou L, Zhong S. Adaptive nonsingular fast terminal sliding mode control for electromechanical actuator[J]. International Journal of Systems Science, 2013, 44(1-3):401-415.Google Scholar
- Wilusz T. Neural networks — A comprehensive foundation[J]. Neurocomputing, 1995, 8 (3):359-360.Google ScholarCross Ref
- Hopfield J. Neural networks and physical systems with emergent collective computational abilities[M]. 1982.Google ScholarCross Ref
- Yang J, Song C F, Song W A, Remote Sensing Image Classification Based on Fuzzy RBF Neural Network Based on Genetic Algorithm[J]. Journal of Chinese Computer Systems, 2018.Google Scholar
- Liu Y, Liu F, Feng H, Frequency tracking control of the WPT system based on fuzzy RBF neural network[J]. International Journal of Intelligent Systems, 2021 (11).Google Scholar
- Wei Kepeng, Hu Jian, Yao Jianyong, Xing Haochen, Le Guigao. Neural Network Fast Terminal Sliding Mode Control of Aviation Electromechanical Actuators[J]. Journal of Aviation Science, 2021, 042(006):105-114.Google Scholar
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