Research On Steering Motor Control Strategy Of Steer-by-wire System

The steer-by-wire system(SBW)eliminates the mechanical connection between the steering system and the wheels,and realizes the steering completely by the electronic control system.It not only improves the active safety performance,driving characteristics and maneuverability of the automobile,but also puts forward new requirements for the core control strategy of the steering motor.Because the driving force of the wheels in response to the driver's intention is fully provided by the steering motor in the steering system,the control of the steering motor is the core of the whole control system.According to the characteristics of the steer-by-wire system,the design of the steering motor control strategy in line with the SBW system is of great significance to realize the rapid completion of the driver's requirements for vehicle steering in the SBW system.This paper takes the steering motor(brushless DC motor)of the steer-by-wire system as the research object,and proposes an improved artificial fish swarm algorithm(IAFSA)to optimize the PID control strategy of Radial Basis Function(RBF)neural network.Finally,the control precision and anti-load capability of the motor with the proposed control strategy are verified by the simulation experiment.The main contents are as follows:(1)The double closed-loop control of the brushless DC motor is studied.Based on the mathematical model of brushless DC motor,various modules of the motor speed control system are built in MATLAB/Simulink,and the double closed-loop speed control system is built.In view of the shortcomings of the traditional PID control method,based on the theoretical study of neural network,the radial basis neural network is introduced into the parameter adjustment of the PID controller,and designs the PID controller based on RBF neural network parameter self-tuning.In MATLAB,the simulation experiment of the motor under two working conditions of speed mutation and load mutation was carried out.The simulation results show that the RBF neural network PID controller has good anti-interference ability and can adapt to the change of motor speed well.(2)Research on optimization of RBF neural network PID control strategy based on an improved artificial fish swarm algorithm.After learning the intelligent algorithm and understanding the optimization principle of artificial fish swarm algorithm,IAFSA was proposed to address the deficiencies of artificial fish swarm algorithm.The global optimal information was added to the position update formula of artificial fish swarm,and the jumping behavior and swallowing behavior of artificial fish were introduced to improve the global search ability of the algorithm.Then,the improved artificial fish swarm algorithm is applied to the RBF neural network PID controller,and the initial parameters of the RBF neural network are trained by using the global search ability of IAFSA to obtain the best initial parameters,so as to enhance the self-learning ability and convergence speed of the network.At last,the superiority of this fusion control algorithm is verified through simulation experiments in MATLAB/Simulink.(3)Test bench construction and control strategy verification.Firstly,the test bed of motor driven control system based on DSP chip TMS320F28335 was built.Then,on the basis of the hardware design of the control system,the embedded code of RBF-PID control algorithm which is optimized by the improved AFSA was implemented,and debugged in Code Composer Studio 6.1 software platform.Finally,the feasibility of the control strategy was preliminarily verified through experiments.Subsequently,based on the working principle of steer-by-wire system,a hardware-in-loop simulation test bed based on xPC Target's steer-by-wire system was built,and a steer-by-wire system joint simulation model of CarSim and MATLAB/Simulink was established.Based on the research of steering motor control strategy with RBF-PID control algorithm which is optimized by the improved AFSA,a hardware-in-loop simulation experiment was carried out,and the superiority of the control algorithm was verified again.