An adaptive control using multiple neural networks for the variable displacement pump

Abstract

A model following adaptive controller made-up by neural networks is proposed to control the angular displacement of swashplate in a variable displacement axial piston pump (VDAPP), which consists of multiple neural networks including a direct neural controller, a neural emulator and a neural tuner. The controls of swashplate angle are investigated by simulation and experiment, serve its model-following characteristics can be evaluated and compared with other methods. © Springer-Verlag Berlin Heidelberg 2006.