@article{Chen_Huang_Huang_Dong_Chen_2026, title={Fuzzy PID Intelligent Control of System Model}, volume={12}, url={http://dx.doi.org/10.22161/ijaems.122.9}, DOI={10.22161/ijaems.122.9}, abstractNote={<jats:p>To improve the control performance of conventional PID controllers in nonlinear and time-varying uncertain systems, a fuzzy neural network PID control algorithm is proposed. This approach integrates the nonlinear control advantages of fuzzy logic with the self-learning and adaptive characteristics of neural networks to achieve real-time online tuning of PID parameters. Based on the mathematical model of an electro-hydraulic servo control system, a fuzzy neural network PID control system model is developed. Taking the control system of a transplanting manipulator as a case study, simulations are conducted using Matlab/Simulink, with the transfer function of the electro-hydraulic servo control system serving as the controlled object. The tracking performance of different control systems is evaluated by comparing traditional PID control, Type-1 fuzzy PID control, interval Type-2 fuzzy PID control, and fuzzy neural network PID control methods employing various membership functions. Simulation and experimental results demonstrate that the fuzzy neural network PID controller exhibits superior control performance compared to traditional PID control, Type-1 fuzzy PID control, and interval Type-2 fuzzy PID control in the target system.</jats:p>}, number={2}, journal={International Journal of Advanced Engineering, Management and Science}, publisher={AI Publications}, author={Chen, Hung Yu and Huang, Zi Xian and Huang, Ming Hui and Dong, Zou Zheng Hao and Chen, Ho Sheng}, year={2026}, pages={65–71} }