Pneumatic Proportional Position Control System Based on BP Neural Networks

2004 ◽  
Vol 471-472 ◽  
pp. 528-531
Author(s):  
Y.J. Liu ◽  
X.Z. Kong ◽  
Z.W. Li
Keyword(s):  
Neural Networks ◽  
Working Conditions ◽  
Pid Controller ◽  
Position Control ◽  
Control Method ◽  
Back Propagation ◽  
Positioning System ◽  
Proportional Valve ◽  
Rod System ◽  
Position Control System

A PID controller based on Back-propagation neural networks is presented and used to the pneumatic proportional positioning system in this paper. A proportional valve-cylinder without rod system for buffering and positioning, which is controlled by microcomputer, is designed and completed in this paper. The experimental results show that the system gains self-adaptability because of the application of this control method. And the buffering and positioning of the cylinder can be implemented under different working conditions.

Condition Monitoring of an Electrohydraulic Position Control System Using Artificial Neural Networks

2004 ◽  
Author(s):  
K. Pollmeier ◽  
C. R. Burrows ◽  
K. A. Edge
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Position Control ◽  
Linear Actuator ◽  
Modular Approach ◽  
Failure Characteristics ◽  
Actuator System ◽  
Artificial Neural ◽  
Position Control System

This paper investigates the condition monitoring of a servo-valve-controlled linear actuator system using artificial neural networks (NNs). The aim is to discuss techniques for the identification of failure characteristics and their source. It is shown that neural networks can be trained to identify more than one fault but these are larger and require more training patterns than networks for single fault diagnosis. This leads to much longer training times and to problems with scaleability. Therefore a modular approach has been developed. Several networks were trained each to identify an individual fault. The parallel outputs of these nets were then used as inputs to another network. This additional network was able to identify not only the correct faults but also the actual fault levels.

RBF-Elman Neural Network Control on Electro-Hydraulic Load Simulator

2013 ◽  
Vol 433-435 ◽  
pp. 1054-1060
Author(s):  
Xiao Hua Wang ◽  
Shuai Wu ◽  
Zong Xiao Jiao
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Control Method ◽  
Back Propagation ◽  
Network Control ◽  
Elman Neural Network ◽  
Simulation Performance ◽  
Neural Network Controller ◽  
Load Simulator ◽  
Load Simulation

Due to load simulation system existing strong disturbance, parameters time-variation and nonlinear, there was low control precision, poor adaptive ability and robustness in traditional control algorithm. In order to improve load simulation performance, The RBF-Elman neural network-based adaptive control method is presented. In this way, the load simulator system is identified by the RBF-Elman neural network identifier, which provides model information (Jacobian matrix) to the PID controller and synchronous compensator in order to make it adaptive. Back-propagation algorithms are used to train neural network. The PID controller which is designed by requirement for steady can overcome the shortcoming of the neural network controller. Finally, the simulations confirm that this control scheme results in a quick response, robustness, and excellent ability against disturbance.

Comparison of Servo Positioning Performance of Pneumatic Cylinders Using Proportional Valve Method and PWM Control Method

2014 ◽  
Vol 541-542 ◽  
pp. 1233-1237 ◽  
Author(s):  
B. Mohan ◽  
D. Saravanakumar
Keyword(s):  
Flow Rate ◽  
High Speed ◽  
Position Control ◽  
Control Method ◽  
Control Technique ◽  
Pwm Control ◽  
Proportional Valve ◽  
Frictional Forces ◽  
Accurate Position ◽  
Pneumatic Cylinders

Servo pneumatics is a mechatronic approach that enables accurate position control of pneumatic drives with high speed. The current study presents a comparison of two servo techniques for position control of pneumatic cylinders. One method uses a proportional valve and another method uses PWM controlled solenoid valves. Mathematical models of the system including the nonlinearities such as mass flow rate, pressure in cylinder chambers and frictional forces are presented in this paper. Using the simulation model created in Matlab-Simulink software, the two systems are analyzed for tracking performances. From the responses it is observed that the proportional valve technique has better tracking characteristics than PWM control technique.

scholarly journals Low Cost Position Controller for EGR Valve System

2018 ◽  
Author(s):  
Habib Bhuiyan ◽  
Jung-Hyo Lee
Keyword(s):  
Coulomb Friction ◽  
Position Control ◽  
Control Method ◽  
Low Cost ◽  
Static Friction ◽  
Automotive Application ◽  
Valve System ◽  
Linear Controller ◽  
Position Controller ◽  
Position Control System

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

scholarly journals Adaptive Inverse Neural Network Based DC Motor Speed and Position Control Using FPGA

2018 ◽  
Vol 11 (3) ◽  
pp. 71-78
Author(s):  
Aula N. Abd
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Back Propagation ◽  
Dc Motor ◽  
Motor Speed ◽  
Back Propagation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Field Programmable

In this research two types of controllers are designed in order to control the speed and position of DC motor. The first one is a conventional PID controller and the other is an intelligent Neural Network (NN) controller that generate a control signal DC motor. Due to nonlinear parameters and movable laborers such saturation and change in load a conventional PID controller is not efficient in such application; therefore neural controller is proposed in order to decreasing the effect of these parameter and improve system performance. The proposed intelligent NN controller is adaptive inverse neural network controller designed and implemented on Field Programmable Gate Array (FPGA) board. This NN is trained by Levenberg-Marquardt back propagation algorithm. After implementation on FPGA, the response appear completely the same as simulation response before implementation that mean the controller based on FPGA is very nigh to software designed controller. The controllers designed by both m-file and Simulink in MATLAB R2012a version 7.14.0.

scholarly journals Adaptive PID Control and Its Application Based on a Double-Layer BP Neural Network

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1475
Author(s):  
Ming-Li Zhang ◽  
Yi-Jie Zhang ◽  
Xiao-Long He ◽  
Zheng-Jie Gao
Keyword(s):  
Double Layer ◽  
Working Conditions ◽  
Pid Control ◽  
Control Method ◽  
Back Propagation ◽  
Engineering Application ◽  
Hydraulic Drive ◽  
Control Parameters ◽  
Hydraulic Drive Unit ◽  
The Relationship

In this paper, focusing on the inconvenience of variable value PID based on manual parameter adjustment for the hydraulic drive unit (HDU) of a legged robot, a method employing double-layer back propagation (BP) neural networks for learning the law of PID control parameters is proposed. The first layer is used to learn the relationship between different control parameters and the control performance of the system under various working conditions. The second layer is used to study the relationship between the parameters of the working conditions and the optimizing control parameters under various working conditions. The effectiveness of the proposed control method was verified by simulation and experiment. The results showed that the proposed method can provide a theoretical and experimental basis for the selection of control parameters, and can be extended to similar controllers, therefore possessing engineering application value.

Position Control System of Autonomous Underwater Vehicle using PID Controller

2021 ◽  
Author(s):  
Ike Bayusari ◽  
Albert Mario Alfarino ◽  
Hera Hikmarika ◽  
Zaenal Husin ◽  
Suci Dwijayanti ◽  
...  
Keyword(s):  
Control System ◽  
Pid Controller ◽  
Position Control ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Position Control System
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