TY  - EJOU
AU  - Zhou, Liang 
AU  - Hu, Qiyao 
AU  - Peng, Xianlin 
AU  - Liu, Qianlong 

TI  - Real-Time Proportional-Integral-Derivative (PID) Tuning Based on Back Propagation (BP) Neural Network for Intelligent Vehicle Motion Control
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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 in managing 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. The trained network is then combined with the PID controller to form a hybrid control strategy. The output layer of the neural network directly adjusts the PID parameters (, , ), 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.
KW  - PID control; backpropagation neural network; hybrid control; nonlinear dynamic processes; edge intelligence

DO  - 10.32604/cmc.2025.061894