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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.

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.