Abstract
Considerable research has been conducted on the control of pneumatic systems. However, nonlinearities continue to limit their performance. To compensate, advanced nonlinear and adaptive control strategies can be used. But the more successful advanced strategies typically need a mathematical model of the system to be controlled. The advantage of neural networks is that they do not require a model. This paper reports on a study whose objective is to explore the potential of a novel adaptive on-line neural network compensator (ANNC) for the position control of a pneumatic gantry robot. It was found that by combining ANNC with a traditional PID controller, tracking performance could be improved on the order of 45% to 70%. This level of performance was achieved after careful tuning of both the ANNC and PID components. The paper sets out to document the ANNC algorithm, the adopted tuning procedure, and presents experimental results that illustrate the adaptive nature of NN and confirms the performance achievable with ANNC. A major contribution is demonstration that tuning of ANNC requires no more effort than the tuning of PID.
Highlights
A large body of research is devoted to improving the performance of servo pneumatic systems
This paper reports on a study whose objective is to explore the potential of a novel adaptive on-line neural network compensator (ANNC) for the position control of a pneumatic gantry robot
It was found that by combining ANNC with a traditional PID controller, tracking performance could be improved on the order of 45% to 70%
Summary
A large body of research is devoted to improving the performance of servo pneumatic systems. Standalone results in which the length of the connecting lines were varied showed good performance for time delays that ranged from 0.012 to 0.12 sec. As this is the level of delay experienced with the application in this paper, it further confirms the potential for a NN approach to the problem at hand. The paper sets out to document the ANNC algorithm, the adopted tuning procedure, and presents experimental results that illustrate the adaptive nature of NN and confirms the level of performance achievable with ANNC
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