A Sliding Mode Control Method for Manipulator Based on GA Optimize Neural Network

2018 ◽  
Author(s):  
Jing Liu ◽  
Jiexin Pu ◽  
Chi Zhang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Mode Control

scholarly journals A Modified RBF Neuro-Sliding Mode Control Technique for a Grid Connected PMSG Based Variable Speed Wind Energy Conversion System

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Rostand Marc Douanla ◽  
Godpromesse Kenné ◽  
François Béceau Pelap ◽  
Armel Simo Fotso
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Control Technique ◽  
Nonlinear Controller ◽  
Parameter Uncertainties ◽  
Power Extraction ◽  
Mode Control ◽  
The Neural Network

A modified control scheme based on the combination of online trained neural network and sliding mode techniques is proposed to enhance maximum power extraction for a grid connected permanent magnet synchronous generator (PMSG) wind turbine system. The proposed control method does not need the knowledge of the uncertainty bounds nor the exact model of the nonlinear system. Since the neural network is trained online, the time to estimate good weights can affect the dynamic performance of the process during the startup phase. Therefore an appropriate way to smoothly and explicitly accelerate the neural network rate of convergence during the startup phase is proposed. Furthermore, a flexible grid side voltage source converter control structure which can handle both grid connected and standalone modes based on conventional proportional integral (PI) control method is presented. Simulations are done in Matlab/Simulink environment to verify the effectiveness and assess the performance of the proposed controller. The results analysis shows the superiority of the proposed RBF neuro-sliding mode controller compared to a nonlinear controller based on sliding mode control method when the system undergoes parameter uncertainties.

Synchronization of Chaotic Gyro Systems via Sliding Mode Control with Cooperative Weights Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 1101-1104
Author(s):  
Yan Qiu Che ◽  
Ting Ting Yang ◽  
Xiao Qin Li ◽  
Rui Xue Li
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Closed Loop ◽  
Control Method ◽  
Sliding Mode ◽  
Closed Loop System ◽  
External Disturbances ◽  
Mode Control ◽  
Nonlinear Uncertainties ◽  
Stability Method

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

scholarly journals Path following of ship based on sliding mode control with improved RBF neural network and virtual circle

2021 ◽  
Vol 39 (1) ◽  
pp. 216-223
Author(s):  
Zongxuan Li ◽  
Renxiang Bu ◽  
Hugan Zhang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Rbf Neural Network ◽  
Path Following ◽  
Nonlinear Observer ◽  
Measured Velocity ◽  
Mode Control ◽  
Tangent Function

To address the unmeasured velocity, external disturbance and internal model uncertainty for following the path of an under-actuated ship, the paper presents a sliding mode control method based on the radial basis function(RBF) neural network and the velocity observer. To enhance the RBF performance of approximating the unknown, an arc tangent function was exploited in the RBF neural network to update its weight values. Then, the nonlinear observer was built via the hyperbolic tangent function to deal with the unmeasured velocity of the ship. Furthermore, in order to avoid overshoots when the ship is moving to its way points, the virtual paths of a variable circle based on the turning angle were designed at the joints of the path of the ship to enhance its path following capability. Finally, the simulation results show that the sliding mode controller designed in the paper can force the ship to follow accurately the reference path in case of time-varying disturbances without measured velocity and enhance the path following performance of the ship and the accuracy of the RBF neural network, thus demonstrating its effectiveness.

scholarly journals Controller Design for Three-Axis Stabilized Platform Using Adaptive Global Fast Terminal Sliding Mode Control with Non-Linear Differentiator

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6532
Author(s):  
Wenlong Feng ◽  
Xiangyin Zhang
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Control Method ◽  
Sliding Mode ◽  
Terminal Sliding Mode Control ◽  
Terminal Sliding Mode ◽  
Mode Control ◽  
Fast Terminal Sliding Mode ◽  
The Stability ◽  
Stabilized Platform

A neural network-based global fast terminal sliding mode control method with non-linear differentiator (NNFTSMC) is proposed in this paper to design the dynamic control system for three-axis stabilized platform. The dynamic model of the three-axis stabilized platform is established with various uncertainties and unknown external disturbances. To overcome the external disturbance and reduce the output chatter of the classical sliding mode control (SMC) system, the improved global fast terminal sliding mode control method using the nonlinear differentiator and neural network techniques is proposed and implemented in the three-axis stabilized platform system. The global fast terminal sliding mode controller can make the controlled state approach to the sliding surface in a finite time. To eliminate the system output chatter, the nonlinear differentiator is employed to obtain the differentiation of the signal. The neural network is introduced to estimate the uncertainties disturbances to improve the stability and the robustness of the control system. The stability and the robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNFTSMC method is verified and compared with the classical proportion-integral-differential (PID) controller, SMC controller and fast terminal sliding mode controller (FTSMC) through the computer simulation. Results validate the effectiveness and robustness of the proposed NNFTSMC method in presence of uncertainties and unknown external disturbances.

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