基于遗传算法优化的采摘末端执行器模糊PID控制研究

Researchon Fuzzy PID control of picking end effector optimization based on improved genetic algorithm

当前,采摘机器人末端执行器普遍存在PID控制参数整定效率低下、控制系统存在非线性特性以及抗干扰能力不足等问题,难以实现精确控制的果实抓取。为此,通过遗传算法对模糊PID控制器的初始参数、量化因子和遗传因子进行优化,实现对控制器整体性能的提升。进行了MATLAB/Simulink软件仿真和现场验证试验,结果显示:GA优化的模糊控制算法可在0.419 s内快速实现稳定抓取力。相较于传统PID控制算法和模糊PID算法,超调量分别减少7.49%和10.05%,稳态误差减少0.24 N和0.09 N。提出的改进PID算法显著提升了末端执行器控制系统的瞬态响应速度、控制精度及稳定性,验证了其在实际抓取应用中的有效性和可靠性。

Currently,the end effectors of harvesting robots commonly suffer from problems such as low efficiency in PID control parameter tuning,nonlinear characteristics,and insufficient anti-interference ability,making it difficult to achieve precise control of fruit harvesting.Therefore,genetic algorithm was used to optimize the initial parameters,quantization factors,and genetic factors of the fuzzy PID controller to improve the overall performance of the controller.MATLAB∕Simulink software simulation field verification experiments were conducted,and the results showed that the improved PID control algorithm could quickly achieve stable grasping force within 0.419 s.Compared with traditional PID control algorithms and manually tuned fuzzy PID algorithms,the overshoot was reduced by 7.49%and 10.05%respectively,and the steady-state error was reduced by 0.24 N and 0.09 N.The proposed improved PID algorithm significantly enhances the transient response speed,control accuracy,and stability of the end effector control system,verifying its effectiveness and reliability in practical grasping applications.