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Research On Control Of Steer-by-Wire System

Posted on:2021-01-17Degree:MasterType:Thesis
Country:ChinaCandidate:H W GuoFull Text:PDF
GTID:2392330611966048Subject:Mechanical engineering
Abstract/Summary:PDF Full Text Request
The Steer-by-Wire(SBW)system replaces the mechanical structure with the electric control unit,so that it could achieve the better performance of steering and reduce the influence of vehicle speed to the steering characteristics caused by fixed steering ratio,which improves the handling stability and active safety of vehicle.Moreover,through reasonable design of adaptive Steer-by-Wire system control strategy,the real-time compensation of wheel angle is realized according to state feedback and parameter estimation of the vehicle,which is beneficial to relieve driving fatigue and improve steering efficiency.For the motorization of vehicle,Steer-by-Wire system,Drive-by-Wire system,Brake-by-Wire system and other systems constitute the Chassis-by-Wire,which is conducive to reducing the mutual interference between various subsystems and improving the comprehensive performance of the Chassis-by-Wire.And for the automation of vehicle,the Steer-by-Wire system can be applied to automatic driving vehicles,in which the steering executive controller receives the angle signals from sensors or upper controllers to realize the automatic steering of the vehicles.To sum up,the research on the control strategy of Steer-by-Wire system is conducive to the development of motorization and automation of vehicle,which is very important and significantAiming at the problem of insufficient stability and robustness when the traditional control method is applied to Steer-by-Wire system,this paper researched on the adaptive Steer-by-Wire execution control strategy.Based on a direct-drive Steer-by-Wire system structure,the neural network and adaptive law are applied with traditional sliding mode control to propose an adaptive steering execution control strategy including lower wheel angle control and upper vehicle stability control.The specific research contents are as follows1.Establish the vehicle dynamics model of Steer-by-Wire systemThe vehicle dynamics model of the Steer-by-Wire system mainly includes the mathematical model of the Steer-by-Wire system,the vehicle dynamics model and the establishment of a co-simulation platform for wheel angle.The mathematical model of the Steer-by-Wire system is established based on MATLAB/Simulink simulation software,including steering motor model,steering transmission model and total interference calculation model.The vehicle dynamics model is built based on CarSim simulation software,which mainly includes the parameter setting and interface definition of the vehicle model.Finally,a co-simulation platform for wheel angle control is established through interface setting,and a wheel angle controller is designed based on PID feedback control,which verifies the feasibility of the co-simulation platform2.Develop the wheel angle control strategy of Steer-by-Wire systemFirstly,the goal of wheel angle control is determined,that an effective adaptive wheel angle control strategy is proposed to realize fast and accurate response of wheel angle when considering system uncertainty caused by the changes of road condition and disturbance of steering motor.In order to solve the problem that the stability and robustness of the Steer-by-Wire system are deteriorated due to system interference when the traditional sliding mode control is applied to the Steer-by-Wire system,an adaptive control strategy using neural network to estimate the parameters of the sliding mode controller is proposed to improve the performance of the wheel angle control.Then the wheel angle controller is designed based on traditional sliding mode and neural network sliding mode respectively,and the performance of the two wheel angle control strategies are compared and analyzed by co-simulation,which verifies the necessity of proposing the adaptive wheel angle control strategy and shows that the neural network sliding mode wheel angle controller has better robustness and stability3.Develop the stability control strategy of Steer-by-Wire systemFirstly,the goal of vehicle stability control is determined,that an effective adaptive stability control strategy is proposed to realize lateral stability control during vehicle steering when considering the uncertainty of vehicle parameters caused by changes in road condition Aiming at the problem that the PID control method is not adaptive enough to control vehicle stability,a stability control strategy combining traditional sliding mode control and on-line adaptive law is proposed to improve the stability and robustness of vehicle lateral stability control.The vehicle stability control strategy based on stable steering yaw rate could correct the expected wheel angle given in advance.Then,a vehicle stability controller is designed based on PID feedback and adaptive sliding mode respectively,and the performances of the two stability control strategies are compared and analyzed by co-simulation,which verifies the necessity of proposing an adaptive vehicle stability control strategy and shows that the adaptive sliding mode vehicle stability controller has better robustness and stability4.Develop the executive control strategy of Steer-by-Wire systemFinally,this paper proposed an adaptive Steer-by-Wire executive control strategy combining the upper adaptive sliding mode stability control strategy and the lower radial basis function neural network sliding mode wheel angle control strategy,and illustrated that the executive control strategy could hold both the vehicle stability and angle controllability through co-simulation.That is,it could not only maintain the lateral stability of the vehicle during steering,but also realize the fast and stable tracking of the wheel angle,when considering the parameter uncertainty and the disturbance of steering motor,which verifies the feasibility and applicability of executive control strategy in Steer-by-Wire system.
Keywords/Search Tags:Steer-by-Wire, Steering Executive Control, Sliding Mode Control, Radial Basis Function Neural Network, Adaptive, Stability Control
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