Modelling and Control of a Pneumatic Motor Using Neural Networks

A considerable amount of interest has been shown by researchers in the control of pneumatic drives over the past decade, for two main reasons, firstly, the response is very slow and the inability to attain set points is high due to hystiresis and secondly, the dynamic model of the system is highly non-linear, which greatly complicates controller design and development. To address these problems, two streams of research efforts have evolved; (i) using conventional methods to develop a modelling and control strategy, (ii) adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modelling and control of an air motor incorporating a pneumatic equivalent of the electric H-bridge. The pneumatic Hbridge has been devised for speed and direction control of the motor. The system is divided into three regions called low speed, medium speed and high speed. The system is highly nonlinear in the low speed region. Linear parametric models characterising the two linearised operating regions (medium and high speed) of the motor are developed using parametric estimation techniques and local controllers are developed using a pole-assignment design. A neuro-model and controller are