Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural Network for Intelligent Vehicle Motion Control  

作　　者：Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu 

机构地区：[1]School of Information Science and Technology,Northwest University,Xi’an,710127,China [2]State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,Northwest University,Xi’an,710127,China [3]Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province,Xi’an,710127,China [4]Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center,Xi’an,710127,China [5]School of Art,Northwest University,Xi’an,710127,China [6]Network and Data Center,Northwest University,Xi’an,710127,China

出　　处：《Computers, Materials & Continua》2025年第5期2375-2401,共27页计算机、材料和连续体(英文)

基　　金：supported by the National Key Research and Development Program of China(No.2023YFF0715103)-financial support;National Natural Science Foundation of China(Grant Nos.62306237 and 62006191)-financial support;Key Research and Development Program of Shaanxi(Nos.2024GX-YBXM-149 and 2021ZDLGY15-04)-financial support,NorthwestUniversity Graduate Innovation Project(No.CX2023194)-financial support;Natural Science Foundation of Shaanxi(No.2023-JC-QN-0750)-financial support.

摘　　要：Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control systems.In modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and safety.However,deficiencies in these systems can lead to significant operational risks.This paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter adjustments.The paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and adaptability.The BP neural network is first trained to capture the nonlinear dynamic characteristics of the vehicle.Thetrained network is then combined with the PID controller to forma hybrid control strategy.The output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight adjustments.Simulation experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 s.Furthermore,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关 键 词：PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]