Intelligent Control Of Hydraulic Servo Driving Position Loop System

Based on the having been finished project of the state "the ninth five years" tackling key scientific and technological problems, this paper makes some detailed research on the continuous casting analogy device in the laboratory of our school. As far as the hydraulic servo position system is concerned, there are parameters variety, outside disturbance and the nonlinear friction in it. In fuzzy control method, it is well known that accurate model of the system is not needed. Moreover, neural network can solve the problems caused by non-linear effectively. As a result, in this paper, the two intelligent control methods are combined to control the hydraulic servo position system, and that has practical meaning and theory value.In this paper, the simplified models derived from the hydraulic system are used to design the controllers. First the summary of the characteristics and application of fuzzy control and neural network theory are narrated. Then, the fuzzy sliding mode controller, the sliding mode controller based on fuzzy self-learning, fuzzy neural network controller based on the modified Elman neural network identification, the fuzzy neural network sliding mode controller and a fuzzy neural network sliding mode controller based on the hydraulic mechanical model are proposed. Where, with Lyapunov function and the state equation that is analyzed from the nonlinear function of servo valve and hydraulic cylinder, the fuzzy-neural network sliding mode controller based on the mechanical model of hydraulic servo system is designed. Consequently, This controller is availability to the nonlinear problem existing in the hydraulic system. At last, The simulation shows that hydraulic servo position control system has good performances in the dynamic and static state control accuracy, thus the feasibility of these control methods are proved effectively.