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There has been considerable interest and activity in the area of application of the artificial neural network (ANN) to hydraulic systems. The pattern recognition capabilities of the ANN has led to an early investigation in areas where the neural networks could be trained using signals that were at least statistically similar to those signals which the trained ANN would be exposed during operation. The dynamic and encompassing nature of hydraulic system signals poses more of a challenge to ANN training and implementation than one of only pattern recognition. However, in the past decade, there has been considerable activity and progress in the application of ANN techniques for hydraulic systems control, identification and condition monitoring. This paper provides an overview of work in this area. The ANN has proven to be a valuable addition to the current existing techniques.

Most fertilizer application systems are not capable of variable rate adjustments “on-the-fly”. To change the application rate, the farmer must dismount the tractor and change the gear ratio mechanically (i.e. via gears, chains, etc.). Air seeder manufacturers have come up with their own unique solutions to address this problem, usually involving electrohydraulics. At present there are older seeding units that perform adequately, but do not have the variable rate option. A retrofit is therefore very desirable for these units. In this paper, the feasibility of a simple hydraulic proportional valve and variable speed motor circuit is employed to replace the gears and chains. The unit is integrated with a microcontroller to provide compensation to the nonlinear properties of a proportional valve, and in turn provide a very accurate feedrate. In addition, direct user input from the cab of the tractor is possible, allowing on-the-go rate changes.

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.

Pulse width modulation (PWM) has been used to alter the performance of on-off hydraulic control valves to make them perform as proportional type flow control valves. Nonlinear performance resulting from time delays in valve switching as well as valve wear due to continuous cycling continue to persist as operational problems. This paper examines a new technique called modified PWM control. The method was found to provide accurate control with a minimum of valve chatter.

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.