Research On Intelligent Control Of Rudder Drive Motor Of Underwater Autonomous Vehicle

The electric steering gear of underwater autonomous vehicle is an important mechanism to realize attitude transformation such as pitch and yaw.The quality of the steering gear control system directly affects the overall control performance of the vehicle.At present,permanent magnet synchronous motors with low loss and high torque are mostly used in the steering gear of underwater vehicles.However,since permanent magnet synchronous motors belong to highorder nonlinear systems,the control effect is greatly disturbed by factors such as electromagnetic and waves.The widely used traditional PID controller is simple and easy to implement,but it does not have the ability to re-tune parameters in the face of complex nonlinear time-varying systems.Therefore,more and more scholars have turned their attention to the strategy of combining intelligent control with traditional PID control methods.In this paper,the fuzzy BP neural network is combined with the traditional PID control technology to simulate and test the permanent magnet synchronous motor speed control system.The results show that compared with the traditional PID controller and the BP neural network PID controller,the motor speed control system based on the fuzzy BP neural network PID intelligent control strategy has better dynamic response characteristics and stronger anti-interference ability.The research results have certain reference value for further research on the controller design of the underwater vehicle control system.Firstly,based on the performance requirements of the underwater vehicle steering gear,based on the motor structure and mathematical model,an intelligent controller combining fuzzy BP neural network and traditional PID is designed to solve the problem that the traditional PID controller is difficult to meet the control accuracy of the permanent magnet synchronous motor.Based on the vector control system,the model of permanent magnet synchronous motor control system is established in Simulink.The control characteristics of traditional PID controller,BP neural network PID controller and fuzzy BP neural network PID controller in pulse load and emergency deceleration are compared and analyzed.The results show that the fuzzy BP neural network PID controller has better anti-interference ability and dynamic response characteristics.At the same time,by establishing the control system of the yaw channel of the underwater vehicle,the rapidity and good followability of the fuzzy BP neural network PID controller are further verified.Secondly,in order to speed up the development of the software control system,the code model of the permanent magnet synchronous motor control system is built based on the embedded encoder toolbox in the Simulink platform.From the local to the whole,the software design of the control algorithm based on DSPF28335 is gradually completed,eliminating the process of writing code and improving the development efficiency.The hardware control system is built with DSPF28335 as the control chip,and the related hardware circuit is designed.Finally,the permanent magnet synchronous motor speed control test system is built to verify the traditional PID controller,BP neural network PID controller and fuzzy BP neural network PID controller.The motor was tested under two working conditions of sudden load and acceleration and deceleration.According to the experimental results of speed,it can be seen that the effect of using fuzzy BP neural network PID controller in the outer ring of speed is the best,which can quickly reach steady state and has good anti-interference and dynamic response characteristics.It has certain reference value for further research on underwater vehicles and servo control.