Application of Radial Basis Function Neural Network in the Starting Process of Electric Forklift
Since the start process for control system of electric forklift has the characters of nonlinearity and fast time-variety, and routine PID method is difficult to satisfy the nonlinear and variable request. So this paper applied a control strategy based on Radial Basis Function neural network, RBFNN PID, to control the motor through closed-loop control, in order to compensate the perturbation, nonlinearity and outside disturbance of system parameter, and achieve the purpose of a smooth start-up of electric forklift. Proved through the simulation and experiment, this control strategy of starting process can control starting current which is rapid, stable and robust.