Neural – Adaptive Control Based on BackStepping and Feedback Linearization for Electro Hydraulic Servo System

In this study Neural Adaptive based on backstepping and feedback linearization is used for velocity control and recognition of an electro hydraulic servo system(EHSS) in the presence of flow nonlinearities, internal friction and noise. This controller consists of three parts: PID controller, nonlinear controller (i.e. Backstepping or FeedbackLinearization) and neural network controller. The backstepping or feedback linearization controller is utilized to avert the system state in a region where the neural network can be accurately trained to achieve optimal control. The combination ofcontrollers is used for producing a stable system which adapts to optimize performance. It is shown that this technique can besuccessfully used to stabilize any chosen operating point of thesystem with noise and without interference. All derived results are validated by computer simulation of a nonlinearmathematical model of the system. The controllers which introduced have a big range to control the system. We compareboth Neural Adaptive based on backstepping and Neural Adaptive based on feedback linearization controllers result with feedback linearization, backstepping and PID controller.

Keywords:

Electro Hydraulic servo system , backstepping , feedback linearization , EHSS