在果实采摘场景中,由于机械末端执行器控制的爪子抓取力不稳定,果实受力不均匀导致果实被抓坏或者抓不住。针对这一现象本研究设计了基于模糊神经网络PID（FNN-PID）的末端力控制。从电压—转矩传递函数出发,建立电机—传动—末端力的统一传递函数模型,在此基础上分别设计传统PID、模糊PID与FNN-PID三类控制器,并给出在相同工况下的仿真对比。结果表明：在被控对象参数恒定与外部扰动同时存在时,FNN-PID在调节响应时间、超调量与稳态误差上均优于另外两类控制器。通过MATLAB软件给出了可复现的Simulink搭建流程与关键参数设置,为机械爪的优化设计提供理论依据。

In the context of fruit harvesting, the grasping force of the robotic end-effector is often unstable, leading to uneven force distribution on the fruit, which may either damage the fruit or cause it to slip. To address this issue, this study proposes an end-effector force control strategy based on Fuzzy Neural Network PID (FNN-PID). Starting from the voltage-to-torque transfer function, a unified transfer function model of the motor-transmission-end force system is established. On this basis, three types of controllers are designed: traditional PID, fuzzy PID, and FNN-PID, and their performances are compared under identical operating conditions. The results show that when the controlled object parameters remain constant and external disturbances are present, FNN-PID outperforms the other two controllers in terms of adjustment response time, overshoot, and steady-state error. Furthermore, reproducible Simulink implementation procedures and key parameter settings are provided using MATLAB, offering a theoretical foundation for the optimization design of robotic grippers.