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在自主种植机器人中使用PSO调节的非线性模糊PID,模糊PID和常规PID控制器进行智能轨迹跟踪

Gashaye Lewtie Hailu¹, Tefera Terefe Yatayew²

¹Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, Ethiopia. gashay2009@gmail.com.

Scientific reports

|November 25, 2025

概括

这项研究优化了自主种植机器人的轨迹跟踪,使用调节了粒子群优化的Fuzzy PID控制器. 拟议的PSO-FPID控制器显著减少了错误,提高了种植精度和农业效率.

科学领域:

机器人技术机器人技术机器人技术
控制系统控制系统
农业工程农业工程

背景情况:

自主机器人需要精确的轨迹跟踪,用于种植种子等任务.
传统的PID控制器可能会与移动机器人的动态和非线性性质作斗争.
模糊PID控制器提供了更好的适应性,但需要有效的参数调.

研究的目的:

为了比较分析三个控制器的轨迹跟踪性能:PSO调节的模糊PID (PSO-FPID),模糊PID (FPID) 和常规PID.
为了优化 Fuzzy PID 控制器的参数,使用粒子集群优化 (PSO) 来提高性能.
评估控制器在最小化差异驱动移动机器人 (DDMR) 在农业应用中的位置和方向错误方面的有效性.

主要方法:

对PSO-FPID,FPID和传统PID控制器进行比较分析.
使用粒子集群优化 (PSO) 调整非线性 Fuzzy PID 控制器参数.
在 MATLAB/Simulink 中模拟控制器性能,使用各种参考轨迹 (直线,圆形等). ) 的情况.
评估性能指标,包括跟踪错误,绝对积分错误 (IAE) 和积分方位错误 (ISE).

主要成果:

与FPID和传统PID相比,PSO-FPID控制器显示的跟踪错误明显较低.
对于直线轨迹,跟踪误差为0.525 (PSO-FPID),1.529 (FPID) 和2.992 (PID).对于直线轨迹,跟踪误差为0.525 (PSO-FPID),1.529 (FPID) 和2.992 (PID).对于直线轨迹,跟踪误差为0.525 (PSO-FPID),1.529 (FPID) 和2.992 (PID).
PSO-FPID实现了卓越的定向控制,显示出增强的稳定性和准确性.

结论:

与PSO调节的Fuzzy PID控制器为自动种植机器人的轨迹跟踪提供了更精确,更可靠的解决方案.
这种加强的控制策略导致更有效的种子放置和提高整体农业效率.
该研究强调了智能优化技术的好处,例如PSO用于调整机器人技术中先进的控制系统.

关键词:

农业机器人农业机器人不同驱动轮的差异驱动轮.模糊的PID (FPID) 是一个模糊的PID.模糊逻辑控制 (FLC) 是一种模糊逻辑控制.障碍物检测障碍物检测障碍物检测在 PIDID 里面.公共服务人员FPID FPID 播种种子播种种子轨迹跟踪跟踪轨道跟踪

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