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The rapid advances in computer technology over the past decade have provided the Fluid Power industry with a means of integrating modern control concepts with high performance hydraulic devices such as servo valves. One such concept, pulse width modulation (PWM), has been around for several decades, but it has been only recently that the full potential of the technique has been realized from an application's point of view. This paper shall consider the use of pulse width modulation techniques as a means of driving a two-stage flapper type servovalve and load. This work presents both analytical and experimental open loop responses of the valve and load under conditions in which variations from an operating point are substantial. The effects of signal filtration of the modulated input are traced through the valve and load by placing the modulation signal at various locations in the valve.

In many areas, neural network technology has made a successful transition from theory to practical application, primarily due to the advances that have been made in computer technology and digital signal processing. Research at the University of Saskatchewan over the past few years has focused on applying neural network technology to fluid power systems. This paper will examine four projects that have been initiated by the authors and their graduate students which use neural networks for purposes of open loop pattern following, multiple input - multiple output control, indirect measurement of actuator displacement, and hydraulic component identification. A brief introduction to static and dynamic neural networks is given. Descriptions of the individual project objectives, the experimental implementation of neural networks to achieve these objectives, and some typical experimental results are considered.