Tuning of an Adaptive Neural Network Compensator for Position Control of a Pneumatic System

2011 ◽  
Author(s):  
Behrad Dehghan ◽  
Sasan Taghizadeh ◽  
Brian Surgenor
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control ◽  
Experimental Results ◽  
Tracking Performance ◽  
Adaptive Neural Network ◽  
Extra Effort ◽  
Adaptive Nature ◽  
Gantry Robot ◽  
Neural Network Compensator

The paper examines the potential of a novel adaptive neural network compensator (ANNC) for the position control of a pneumatic gantry robot. Previousl experimental results were disappointing, with only a 20% improvement in performance when ANNC was employed with a PID controller. The conclusion was that the level of improvement with ANNC did not warrant the extra effort required for implementation. However, when the tests were repeated after the system had been reconfigured, improvements on the order of 45% to 70% were achieved. This paper presents a tuning procedure for ANNC, confirms the adaptive nature and provides results that support the conclusion that ANNC can indeed provide a significant improvement in tracking performance.

scholarly journals A Novel Adaptive Neural Network Compensator as Applied to Position Control of a Pneumatic System

2011 ◽  
Vol 02 (04) ◽  
pp. 388-395 ◽  
Author(s):  
Behrad Dehghan ◽  
Sasan Taghizadeh ◽  
Brian Surgenor ◽  
Mohammed Abu-Mallouh
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Pneumatic System ◽  
Adaptive Neural Network ◽  
Neural Network Compensator

Fuzzy PID and Contour Tracking as Applied to Position Control of a Pneumatic Gantry Robot

2013 ◽  
Author(s):  
James Waldie ◽  
Brian Surgenor ◽  
Behrad Dehghan
Keyword(s):  
Pid Controller ◽  
Position Control ◽  
Fuzzy Controller ◽  
Fuzzy Pid ◽  
Pid Algorithm ◽  
Fuzzy Pid Controller ◽  
Future Work ◽  
Fuzzy Pd ◽  
Gantry Robot ◽  
Fuzzy Adaptive

In previous work, the performance of PID plus an adaptive neural network compensator (ANNC) was compared with the performance of a novel fuzzy adaptive PID algorithm, as applied to position control of one axis of a pneumatic gantry robot. The fuzzy PID controller was found to be superior. In this paper, a simplified non-adaptive fuzzy algorithm was applied to the control of both axes of the robot. Individual step results are first shown to confirm the validity of the simplified fuzzy PID controller. The fuzzy controller is then applied to a sinuosoidal tracking problem with and without a fuzzy PD tracking algorithm. Initial results are considered to be very promising. Future work requires developing an adaptive version of the controller in order to demonstrate robustness relative to changing tracking frequencies and changing supply pressures.

Anti-sway position control of an automated transfer crane based on neural network predictive PID controller

2005 ◽  
Vol 19 (2) ◽  
pp. 505-519 ◽  
Author(s):  
Jin-Ho Suh ◽  
Jin-Woo Lee ◽  
Young-Jin Lee ◽  
Kwon-Soon Lee
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Position Control

Design and implementation of an adaptive neural-network compensator for control systems

2001 ◽  
Vol 48 (2) ◽  
pp. 416-423 ◽  
Author(s):  
Young-Kiu Choi ◽  
Min-Jung Lee ◽  
Sungshin Kim ◽  
Young-Chul Kay
Keyword(s):  
Neural Network ◽  
Control Systems ◽  
Adaptive Neural Network ◽  
Design And Implementation ◽  
Neural Network Compensator

scholarly journals Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Jie Zhao ◽  
Jun Zhong ◽  
Jizhuang Fan
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Phenomenological Model ◽  
Pid Controller ◽  
Position Control ◽  
Rbf Neural Network ◽  
Pneumatic Muscle ◽  
Highly Nonlinear ◽  
Experimental Apparatus ◽  
Pneumatic Muscle Actuator

Pneumatic Muscle Actuator (PMA) has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

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.

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