Design of Intelligent Levelling Control System for Grader

Abstract

For the problem of grader intelligent levelling control system with much uncertainty and serious nonlinearity, precise mathematical model based traditional Proportional Integral-Derivative (PID) controller cannot meet the requirements of angle control. In order to improve dynamic performances of grader intelligent levelling control system in the laser transmitter platform of laser sources positioning system based on dynamic triangle principle, the obliquity sensor to detect the angle of laser transmit platform, and angle sensor data processed based on the data fusion algorithm through federal Kalman fusion, which is compared with Kalman algorithm. And the Radial Basis Function (RBF) Neural Networks based PID control strategy is proposed for the grader level control in this paper. This method can identify the mathematical model of the grader levelling angle via the RBF neural networks, and then the PID parameters can be optimized automatically to accommodate the characteristic variation of the process. The simulation results show that the PID controller designed based on the RBF neural networks has good control performance on the steam generator level control.