Research on Electro-Hydraulic Force Servo System Based on Neural Network and Fuzzy Intelligent Control Strategy

2014 ◽  
Vol 11 (4) ◽  
pp. 1205-1210 ◽  
Author(s):  
Jianying Li ◽  
Yanwei Wang ◽  
Xiaojing Wang ◽  
Junpeng Shao ◽  
Tianye Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Hydraulic Force

Study of Electro-Hydraulic Force Servo System Based on Flow Press Servo Valve and Neural Network Intelligent Control Strategy

2013 ◽  
Vol 427-429 ◽  
pp. 1167-1170
Author(s):  
Jian Ying Li ◽  
Tian Ye Yang ◽  
Yan Wei Wang ◽  
Zhi Yong Mao ◽  
Li Gang Wu
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Mathematic Model ◽  
Load Torque ◽  
Total Load ◽  
Servo Valve ◽  
Hydraulic Force ◽  
The Neural Network

The flow press servo valve has very special work principle, and it now is widely used in the electro-hydraulic force servo system as main and kernel part. According to its special work principle, electromagnetic basic theory and the flow continuity equation, the mechanics analysis of every main part of the flow press servo valve was finished; the author built the mathematic model of the flow press sever valve. During the process of building model, the author took into account the load torque on the torque motor keeper and the total load torque of torque motor. On the other hand, the neural network intelligent control strategy was used in the force servo system to improve the whole system performance. The mathematic model of the electro-hydraulic force servo system controlled by the flow press servo valve with the neural network control strategy was built. The simulation curves and the experiment curves were accord compared by system simulation and experiment results, so we can know also that the mathematic model of the flow press sever valve and the electro-hydraulic force servo system controlled by this kind of valve with the neural network intelligent control strategy are correct.

Study of Electro-Hydraulic Force Servo System Based on Flow Press Servo-Valve and Fussy Intelligent Control Strategy

2013 ◽  
Vol 10 (12) ◽  
pp. 2955-2960 ◽  
Author(s):  
Jianying Li ◽  
Yanwei Wang ◽  
Xiaojing Wang ◽  
Junpeng Shao ◽  
Guihua Han ◽  
...  
Keyword(s):  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Servo Valve ◽  
Hydraulic Force

Electro-Hydraulic Servo Control Based on CMAC Neural Network for Excavating Robot

2010 ◽  
Vol 34-35 ◽  
pp. 825-830
Author(s):  
Qun Liang Dai ◽  
Hong Liang Dai ◽  
Xiao Hai Qu
Keyword(s):  
Neural Network ◽  
Dynamic Model ◽  
Strong Coupling ◽  
Intelligent Control ◽  
Control Strategy ◽  
Servo System ◽  
Servo Control ◽  
Cmac Neural Network ◽  
Hydraulic Servo ◽  
Hydraulic Servo System

In this paper the electric-hydraulic servo system for excavating robot is analysed. The kinematic and the dynamic model of working equipment are established. Aim at the electric-hydraulic servo system of the feature with many variables, strong coupling and non-linear, the CMAC neural network was presented combined with popular PD algorithm, which could realize intelligent control for the working equipment of excavating robot. The result of simulation show that control strategy features higher precision and robustness.

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3146
Author(s):  
Hexu Yang ◽  
Xiaopeng Li ◽  
Jinchi Xu ◽  
Dongyang Shang ◽  
Xingchao Qu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Servo System ◽  
Pole Assignment ◽  
Pi Controller ◽  
Friction Torque ◽  
System Model ◽  
Angle Error ◽  
Assignment Method

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.

scholarly journals Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xuehui Gao
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Neural Control ◽  
Servo System ◽  
High Gain ◽  
Tracking Errors ◽  
Vector Error ◽  
Canonical State ◽  
Adaptive Control Strategy ◽  
High Order Neural Network

An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error e into a scalar error s. Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.

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.

PIDNN Based Intelligent Control of Ignition Oven

2011 ◽  
Vol 396-398 ◽  
pp. 493-497
Author(s):  
Yu Qian Ying ◽  
Jian Gang Lu ◽  
Jin Shui Chen ◽  
You Xian Sun
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Pid Control ◽  
Proportional Integral Derivative ◽  
Labor Costs ◽  
Neural Structure ◽  
Fuel Gas ◽  
Significant Saving ◽  
Proportional Integral

In a steel plant, fuel gas caloricity of ignition oven always changes rapidly and largely. Consequently, the temperature of ignition oven can’t keep steady. To overcome this problem we employ intelligent control of ignition oven based on PIDNN (Proportional-Integral-Derivative Neural Network). As we know, ignition oven is a nonlinear, large delay and slow time-varying process, so traditional PID control usually doesn’t work well. Artificial neural networks can perform adaptive control by learning, so we adopt Proportional-Integral-Derivative neural network to tackle the problem taking the advantages of both PID control and neural structure. In order to satisfy the restrictions of industrial instruments, we combine PIDNN control algorithm with expert system mechanism to fulfill the final intelligent control strategy. At a sintering plant in Hangzhou, we deploy the intelligent control strategy turning out a satisfactory result that the ignition oven temperature can be controlled steadily within a much smaller range with significant saving of labor costs and improving of energy efficiency.

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