Study Of New Control Strategy Of A Gun Electro-hydraulic Servo System

In this paper the research object is the electro-hydraulic servo system, I will use the robust control theory which can achieve the requirements of static and dynamic indicators.The high power weapon servo-system is impossible to be constructed the model which can achieve guideline of the project well because of its inevitable structured and unstructured uncertainties. The paper firstly expounds the electro-hydraulic servo system in the artillery exhibit highly nonlinear behavior. A system model which concludes the nonlinear factors was presented based on the SimHydraulic toolbox of Simulink analysis system. The approach can gain the actual characteristic of the system through simulation. And then a typical multi-layer feed-forward artificial neural networks named BP neural network has been studied. But traditional BP neural network has many defects, such as slow training velocity and converge to a local minimum point, GA-BP algorithm has much better performance. On the basis of the Simulink model, the paper researches the model identification based on neural network, presents the normal structures of neural network identification, discusses the neural network identification for nonlinear system. According to a set of test data of System, the paper has made system identifications of positive model based on neural networks, and analyses identification result.As neural network adaptive control not only has the good robustness as that in the adaptive systems, but also has the ability of self-learning and good fault-tolerant, one adaptive control strategies are introduced in this paper. Combining the adaptive control, the paper researches the neural network adaptive control of electro-hydraulic servo system, and presents simulation results. According to the results of the simulation and experiments, the neural network self-adapting controller has the high quality of performance. It can achieve the guideline of the project well.