Control of a Pneumatic Gantry Robot With Adaptive Neural Network Compensation

2010 ◽  
Author(s):  
Sasan Taghizadeh ◽  
Brian Surgenor ◽  
Mohammed Abu-Mallouh
Keyword(s):  
Neural Network ◽  
Contact Force ◽  
Coulomb Friction ◽  
Tangential Velocity ◽  
Contour Tracking ◽  
Adaptive Neural Network ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Gantry Robot

In a previous paper, the application of a pneumatic gantry robot to contour tracking was examined. A hybrid controller was structured to control the contact force and the tangential velocity, simultaneously. Performance was found to be limited by system lag and Coulomb friction. A neural network (NN) compensator was subsequently developed to counter both effects. Simulation results for straight and curved edge workpieces demonstrated the effectiveness of the NN compensator. This paper validates the results experimentally, highlights the tuning issues associated with an adaptive NN compensator, and confirms the capabilities of a pneumatic gantry robot.

Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking With Neural Network Compensation

2008 ◽  
Author(s):  
Mohammed Abu-Mallouh ◽  
Brian Surgenor
Keyword(s):  
Neural Network ◽  
Coulomb Friction ◽  
Tangential Velocity ◽  
Limiting Factor ◽  
Contour Tracking ◽  
Velocity Control ◽  
Hybrid Controller ◽  
Controller Performance ◽  
Force Velocity ◽  
Gantry Robot

In this paper, the application of a pneumatic gantry robot to contour tracking is examined. A hybrid controller is structured to control the contact force and the tangential velocity, simultaneously. A previous study provided controller tuning and model validation results for a fixed gain PI-based force/velocity controller. Performance was limited by system lag and Coulomb friction. New results demonstrate that even with perfect friction compensation, the limiting factor is the system lag. A neural network (NN) compensator was subsequently developed to counter both effects. Results for straight and curved edged workpieces are presented to demonstrate the effectiveness of the NN compensator and the capabilities of a pneumatic gantry robot.

Hybrid Control of a Pneumatic Gantry Robot for Contour Tracking: Proportional Pressure Versus Proportional Flow Control

2009 ◽  
Author(s):  
Mohammed Abu-Mallouh ◽  
Brian Surgenor ◽  
Sasan Taghizadeh
Keyword(s):  
Flow Control ◽  
Coulomb Friction ◽  
Hybrid Control ◽  
Tangential Velocity ◽  
Pressure Control ◽  
Contour Tracking ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Gantry Robot ◽  
Proportional Flow

The application of a pneumatic gantry robot to contour tracking is examined. A hybrid controller is structured to control the contact force and the tangential velocity, simultaneously. In a previous study, experimental contour tracking results for the robot were obtained with electronic proportional pressure control (PPC) valves. The results demonstrated the potential of pneumatic actuation for contour tracking applications. In another study it was found that improvement in performance was limited by system lag and Coulomb friction. A neural network (NN) compensator was developed to counter both effects. Simulation results demonstrated the effectiveness of the NN compensator. Although improvement in performance with NN compensation was significant, this was offset by the requirement for substantive design effort. This paper shows experimentally that equally significant improvement can be achieved by switching from PPC valves to proportional flow control (PFC) valves. The PFC approach requires less design effort.

Hybrid Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking: Tuning and Model Validation

2007 ◽  
Author(s):  
Mohammed Abu-Mallouh ◽  
Brian Surgenor
Keyword(s):  
Model Validation ◽  
Coulomb Friction ◽  
Tangential Velocity ◽  
Contour Tracking ◽  
Velocity Control ◽  
Negative Effects ◽  
Sufficient Detail ◽  
Force Velocity ◽  
Future Work ◽  
Gantry Robot

The paper examines hybrid force/velocity control of a pneumatic gantry robot for contour tracking. Both experimental and simulation results are presented. The control system is structured to control the contact force and the tangential velocity simultaneously. Controller tuning and model validation results are given for a fixed gain PI-based hybrid force/velocity controller. A simple yet effective model is presented in sufficient detail such that other researchers can perform their own simulations to investigate the utility of their own controller designs. The model is used to demonstrate the negative effects of Coulomb friction. Future work will focus on friction compensation techniques to improve performance.

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 Bifurcation and Hybrid Control for A Simple Hopfield Neural Networks with Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zisen Mao ◽  
Hao Wang ◽  
Dandan Xu ◽  
Zhoujin Cui
Keyword(s):  
Neural Network ◽  
Hopf Bifurcation ◽  
Control Strategy ◽  
State Feedback ◽  
Hybrid Control ◽  
Hopfield Neural Network ◽  
Parameter Perturbation ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Delayed State Feedback

A detailed analysis on the Hopf bifurcation of a delayed Hopfield neural network is given. Moreover, a new hybrid control strategy is proposed, in which time-delayed state feedback and parameter perturbation are used to control the Hopf bifurcation of the model. Numerical simulation results confirm that the new hybrid controller using time delay is efficient in controlling Hopf bifurcation.

scholarly journals Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

CONVERTER ◽  
2021 ◽  
pp. 709-715
Author(s):  
Peibo Li, Peixing Li, Chen Yanpeng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Tracking Error ◽  
Network Control ◽  
Neural Network Control ◽  
Robot Arm ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Simulation Results

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.

scholarly journals An Intelligent Evaluation Method to Analyze the Competitiveness of Airlines

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Xumei Chen
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Evaluation Method ◽  
Standard Sample ◽  
Reference Value ◽  
The Self ◽  
Index Data ◽  
Som Neural Network ◽  
Simulation Results ◽  
Self Organizing

An intelligent evaluation method is presented to analyze the competitiveness of airlines. From the perspective of safety, service, and normality, we establish the competitiveness indexes of traffic rights and the standard sample base. The self-organizing mapping (SOM) neural network is utilized to self-organize and self-learn the samples in the state of no supervision and prior knowledge. The training steps of high convergence speed and high clustering accuracy are determined based on the multistep setting. The typical airlines index data are utilized to verify the effect of the self-organizing mapping neural network on the airline competitiveness analysis. The simulation results show that the self-organizing mapping neural network can accurately and effectively classify and evaluate the competitiveness of airlines, and the results have important reference value for the allocation of traffic rights resources.

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