Control of Hydraulic Power System by Mixed Neural Network PID in Unmanned Walking Platform

Abstract

To speedily regulate and precisely control a hydraulic power system in a unmanned walking platform (UWP), based on the brief analysis of digital PID and its shortcomings, dual control parameters in a hydraulic power system are given for the precision requirement, and a control strategy for dual relative control parameters in the dual loop PID is put forward, a load and throttle rotation-speed response model for variable pump and gasoline engine is provided according to a physical process, a simplified neural network structure PID is introduced, and formed mixed neural network PID(MNN PID)to control rotation speed of engine and pressure of variable pump, calculation using the back propagation(BP) algorithm and a self-adapted learning step is made, including a mathematic principle and a calculation flow scheme, the BP algorithm of neural network PID is trained and the control effect of system is simulated in Matlab environment, real control effects of engine rotation speed and variable pump pressure are verified in the experimental bench. Results show that algorithm effect of MNN PID is stable and MNN PID can meet the adjusting requirement of control parameters.