Study on Self-tuning of Robot Parameters for EMC Vehicle Steering Test

With the continuous advancement of electric vehicles and smart internet technologies, ensuring vehicle safety through electromagnetic compatibility (EMC) testing in complex electromagnetic environments has become increasingly critical. However, due to the significant variability in vehicle response characteristics under electromagnetic interference, traditional PID control methods for steering robots struggle to meet the high-precision requirements of such tests. In this study, a novel fuzzy PID parameter self-tuning method is proposed, leveraging a Sparrow Search Algorithm-Back Propagation (SSA-BP) neural network. This method optimizes the fuzzy controller's quantization factor by constructing a neural network system where the expected motor angle serves as the input and the quantization factor as the output. The quantization factor is then calibrated online through iterative training.The proposed approach enables the steering robot to achieve real-time, adaptive tuning of the PID parameters for the drive motor by adjusting the steering torque according to different vehicle characteristics, thereby enhancing the robot's anti-interference capability and robustness in EMC testing. The effectiveness of this method is validated through Matlab/Simulink simulations, experiments conducted on the Sensodrive platform, and tests performed in an EMC anechoic chamber. The results indicate that the method offers substantial improvements in control accuracy and anti-interference capabilities, highlighting its strong potential for practical application.