Simulation Research On Servo Control System Of Taxi Light Based On Improved Intelligent Algorithm

The continuous maturity of automation and mechatronics intelligent integration technology indicates the gradual enhancement of the all-round intelligent and humanized industrial development trend.Intelligent lighting technology is also improving day by day,and the fixed mode of aircraft taxi lights cannot meet the driver’s needs when turning at night.Safety and comfort requirements.Therefore,a position-following steering system for taxi lights is proposed.Based on the application background,through studying the working principles and control methods of brushed DC servo motors and brushless DC motors,the PID controller,Fuzzy PID controller,characteristic observer compensation controller,BP neural network PID controller and BP neural network PID controller based on fuzzy coefficient correction.The two types of servo control systems are simulated and analyzed sequentially,and their response characteristics such as response speed,control acc uracy and anti-interference ability are compared.On the premise of establishing the corner model of the lamp according to the actual needs,firstly,the DC servo motor is used as the research object,and the dynamic model and its closed-loop transfer function are established by analyzing its working principle,and then the Simulink tool library in Matlab is used to build the control system simulation model.Observe and analyze the response characteristics of the servo motor under step signal and sinusoidal signal input.Then take the brushless DC motor as the research object,establish its position-speed-current three closed-loop control system.The space vector method is used to drive the motor,and the advantages and disadvantages of the position controll er under different algorithms are compared.In this framework,based on the consideration of multiple dimensions such as the position accuracy,response speed,anti-interference ability and dynamic and static stability of the taxi light follow-up steering angle,the main influencing factors leading to the poor output of the system are analyzed and a friction model and model based on friction are established.The fuzzy PID control compensated by the load model exponential convergence observer,through simula tion and comparison,highlights the shortcomings of several conventional algorithms and their improvement strategies.Then combine BP neural network and fuzzy theory to establish a new PID compound control algorithm.In view of the problems of slow converg ence speed,difficulty in obtaining training samples,and easy to fall into local extremes in BP neural network,according to the compensation control theory,a correction coefficient is introduced in the node position between the forward network and the r everse adjustment of the neural network,and fuzzy control is used.The controller makes further online adjustments,and the simulation verification: the improved intelligent control algorithm has strong anti-interference ability and signal tracking abilit y,and the response speed is not lower than the conventional control strategy.Finally,complete the software design according to the system structure and build a hardware test platform with STM32F405 microprocessor and brushless DC motor as the core,and get the results by tuning the parameters of the inner loop controller : it is concluded that the torque current component is compared with the flux current component.The controller parameters have a very significant adjustment effect on the transient and steady-state characteristics of the system,thus confirming the rationality of the previous examination of the system performance with torque disturbance as the main factor,which proves the feasibility and superiority of this improved intelligent control algorithm in this low-speed position control system.