Improvement of a hydrostatic transmission control system performence using radial basis neural network

Abstract EN

Pump-controlled motors (PCM) are the preferred power elements in most applications because of their high maximum operating efficiency.

The dynamics of such hydraulic systems are highly nonlinear and the system may be subjected to non-smooth and discontinuous nonlinearities.

Aside from the nonlinear nature of hydraulic dynamics, hydraulic servo systems also have large extent of model uncertainties such as uncompensated friction forces variation of system parameters and external disturbances.

The conventional Proportional, Integral and Derivative (PID) controller cannot cope with hydraulic system nonlinearities and could not compensate its variation of parameters.

Therefore, a radial basis neural network has been suggested to control the speed response of PCM.

The structure of radial basis neural network (RBNN) controller is simple and efficient in control purposes.

The design of control surface based on radial basis function (RBF) controller has been considered.

The performance of PID and RBF controllers has been assessed based on the improvement in speed behavior and their capabilities to compensate the changes in system parameters (load and bulk of modulus).

Also, the effect of tuning of the radial basis parameters on the dynamic response has been studied.

Results showed that the RBF controller is more robust and shows typical results compared to classical PID controller.

Moreover, a further improvement in speed dynamic can be obtained with appropriate tuning of RBF parameters.