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 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

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.

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