Modeling and Simulation of an Air Motor using Elman Neural Networks

R. Marumo and M.O. Tokhi (UK)


: Air motor, neuromodeling, neural networks.


The past decade has seen an increase in research activities in control of pneumatic drives. Motivation for this kind of study is that, the response of a pneumatic drive is very slow which leads to inability of the system to attain set points due to high hysteresis. Also the dynamic model of the pneumatic system is highly nonlinear, which greatly complicates controller design and development. To address these problem areas, two streams of research efforts have evolved. These are: using conventional methods to develop a modeling control strategy and adopting a strategy that does not require mathematical model of the system. This paper presents an investigation into the modeling and control of an air motor incorporating a pneumatic equivalent of the electric H bridge. The pneumatic H-bridge has been devised for speed and direction control of the motor. The system characteristics are divided into three main regions called low speed, medium speed and high speed. The system is highly non-linear in the low speed region and hence a neuro-model and controller with Elman networks is proposed.

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