scholarly journals Complementary Sliding Mode Control via Elman Neural Network for Permanent Magnet Linear Servo System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 82183-82193 ◽  
Author(s):  
Hongyan Jin ◽  
Ximei Zhao
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Permanent Magnet ◽  
Sliding Mode ◽  
Servo System ◽  
Elman Neural Network ◽  
Mode Control

Second Order Sliding Mode Control of Direct-Drive Ring Permanent Magnet Torque Motor

2011 ◽  
Vol 383-390 ◽  
pp. 5964-5971 ◽  
Author(s):  
Yi Biao Sun ◽  
Ya Nan Jing ◽  
Jia Kuan Xia
Keyword(s):  
Sliding Mode Control ◽  
Permanent Magnet ◽  
Sliding Mode ◽  
Servo System ◽  
Second Order ◽  
Continuous Control ◽  
Direct Drive ◽  
Mode Control ◽  
Twisting Algorithm ◽  
Second Order Sliding Mode

The direct-drive ring permanent magnet torque motor is easily affected by parameters changes and the load torque disturbances, which reduces the servo performance of the system. In order to enhance the robustness of the servo system, the super twisting algorithm based on the second order sliding mode control (SMC) is proposed as the speed controller of the direct-drive servo system. The super twisting algorithm need not know the information of the sliding mode time derivative, which through the continuous control measure the sliding mode and its derivative approach zero in finite time. This method not only guarantees the robustness of the servo system and eliminates chatting, but also enhances the static precision of the servo system. The simulation results show that the servo system of the direct-drive NC rotary table has a very strong robustness by adopting the control method against parameters changes and the external disturbances.

scholarly journals RBF Neural Network Sliding Mode Control Method Based on Backstepping for an Electro-hydraulic Actuator

2020 ◽  
Vol 66 (12) ◽  
pp. 697-708
Author(s):  
Wending Li ◽  
Guanglin Shi ◽  
Chun Zhao ◽  
Hongyu Liu ◽  
Junyong Fu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Strategy ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Servo System ◽  
Control Law ◽  
Hydraulic Actuator ◽  
Mode Control ◽  
Non Linear System

Aiming at the interference problem and the difficulty of model parameter determination caused by the nonlinearity of the valve-controlled hydraulic cylinder position servo system, this study proposes a radial basis function (RBF) neural network sliding mode control strategy based on a backstepping strategy for the electro-hydraulic actuator. First, the non-linear system model of the third-order position electro-hydraulic control servo system is established on the basis of the principle analysis. Second, the model function RBF adaptive law and backstepping control law are designed according to Lyapunov’s stability theorem to solve the problem of external load disturbance and modelling uncertainty, combined with sliding mode control strategy and virtual control law. Finally, simulation and experiment on MATLAB Simulink and semi-physical experimental platform are accomplished to show the effectiveness of the proposed method. Moreover, results show that the designed controller has high tracking accuracy to the given signal.

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