Search Results

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.

In much heavy industrial equipment, power transfer is primarily provided by a hydraulic system. Reliable operation of a hydraulic circuit can only be ensured through proper design, effective maintenance and continuous monitoring. Each of these areas requires the expertise of well-trained and experienced individuals. Unfortunately such individuals are not always available. However, the recently introduced technology of “expert” or “knowledge-based” systems can make these individuals' expertise available in the form of a computer program. As part of a long term project in the application of expert systems to hydraulic circuit design, a prototype program has been developed which can successfully configure a variety of circuits including multiple load and add-on circuits. This is currently being expanded to include sequenced circuits, steady-state circuit analysis, which would include component sizing, and dynamic analysis.