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

Abstract

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.