A Blasting Otter Electro-hydraulic Servo System Modeling And Control Study

Electro-hydraulic servo system of explosive minesweepers is a typical nonlinear time-varying system, traditional modeling and control methods are difficult to obtain satisfactory control effect. The neural network has unlimited capacity for approaching any nonlinear function. By using neural network for modeling the nonlinear system can overcome the shortcomings of the traditional method system modeling. PID controller designed from the proportion, integration, differential of the deviation signal is the most common type of controller. Fuzzy control is based on the control of the language control rule, it does not need precise mathematical model of system. It can improve the performance of the PID control that uses fuzzy rule for tuning PID parameters. In this paper, the system is modeled by neural network and uses fuzzy self-tuning PID controller to control the system.Firstly, the works of the systems and hardware circuit of the electro-hydraulic servo system of explosive minesweepers is introduced, and the hydraulic circuit of the system is designed. A joint simulation data acquisition model had been built in the AMESim software and Simulink software by the parameters of the main components of the system, Input and output data were collected when co-simulation was done.Radical Basis Function (RBF), genetic algorithms (GA) to optimize the two parameters of the Radical Basis Function, genetic algorithms to optimize the three parameters of the Radical Basis Function were used to settle the modeling of the systems. In order to compare the performance evaluation of the model, the Root Mean Square Error (RMSE) and the Variance Accounted For (VAF) were regarded as the performance indicators. The modeling of third method has little error, strong anti-interference ability.Finally, fuzzy PID were used to solve the control problem of explosive minesweepers. Step signal and sinusoidal signal were input the model to simulate the work of the systems.According to the graph of tracking and the error, the controller has excellent effects, systems response speed and so on indicators able to meet the systems performance requirements.