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对PSO-PID和PSO-FLC用于连续机器人开发的建模和控制的性能评估
Elsayed Atif Aner1,2, Mohammed Ibrahim Awad3, Omar M Shehata3
1Department of Mechatronics Engineering, Egyptian Russian University (ERU), Badr, 11829, Cairo, Egypt. alsayedatif88@gmail.com.
Scientific reports
|January 6, 2024
概括
本研究介绍了一种新的动态模型和针对连续机器人的优化控制器. 粒子群集优化显著提高了PID和模糊逻辑控制器的轨迹跟踪精度.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统工程 控制系统工程
背景情况:
- 连续机器人是复杂的,非线性系统,需要先进的建模和精确运动的控制.
- 传统的控制方法与这些机器人的高自由度和合动态作斗争.
研究的目的:
- 为连续机器人开发一个系统动态模型,考虑弹性和重力.
- 优化反向动态控制器 (PID和FLC) 使用粒子群优化 (PSO) 进行增强的轨迹跟踪.
主要方法:
- 一个基于欧勒-拉格朗日公式的系统动态模型被开发出来,用断片式恒定曲率 (PCC) 假设.
- 使用粒子群优化 (PSO) 算法调整PID和模糊逻辑控制器 (FLC).
- 绝对误差的积分时间 (ITAE) 作为基于PSO的优化目标函数.
主要成果:
- 与标准PID控制器相比,PSO优化的PID控制器在上升时间,超速和沉降时间方面取得了显著的改进.
- 优化PSO的模糊逻辑控制器 (PSO-FLC) 表现出卓越的性能,实现了0.7s的沉降时间和0.4s的上升时间.
- PSO-FLC实现了最低的ITAE错误,超过了标准的FLC和PSO-PID控制器.
结论:
- 拟议的动态模型和PSO优化的控制器提高了连续机器人轨迹跟踪的精度和性能.
- 在评估的方法中,PSO-FLC为控制连续机器人提供了最高的准确性和效率.
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