基于改进的灰狼优化为假肢手的自适应LQR控制的设计
Khaled Ahmed1,2, Ayman A Aly3,2, Mohamed O Elhabib4,2
1Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Biomimetics (Basel, Switzerland)
|July 25, 2025
概括
这项研究介绍了一种改进的灰狼优化 (IGWO) 调整的线性正方体调节器 (LQR),用于先进的多指机器人手 (MFRH) 控制. IGWO-LQR显著提高了辅助技术的精度和动态响应.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 辅助技术 辅助技术 辅助技术
- 优化算法 优化算法
背景情况:
- 多指机器人手 (MFRHs) 是对上肢残疾人的重要辅助技术.
- 精确和稳定的MFRH控制仍然是一个重要的工程挑战.
- 现有的控制方法往往难以满足对高性能机器人手操作的需求.
研究的目的:
- 通过优化控制策略,提高MFRH的控制性能.
- 系统调整一个线性方位调节器 (LQR) 控制器,以提高精度和稳定性.
- 在各种操作条件下,根据已确定的方法验证拟议控制器的有效性.
主要方法:
- 使用德纳维特-哈顿伯格动力学和欧勒-拉格朗日方程,开发了MFRH的动态模型.
- 采用了改进的灰狼优化 (IGWO) 算法来系统地确定LQR控制器的最佳权重矩阵.
- 将IGWO调整的LQR与比例整数导数 (PID) 和带有粒子群优化 (PID-PSO) 控制器的PID进行了基准测试.
主要成果:
- 与PID和PID-PSO控制器相比,IGWO-LQR控制器实现了显著更快的定位时间和步进输入的零超越.
- 在所有关节中,IGWO-LQR表现出卓越的性能,这是3关节的整合绝对误差 (IAE) 较低 (0.001334对PID的0.004695) 的证据.
- 在正方形波,正弦波和西格米波输入下,稳定性得到证实,IGWO-LQR始终提供最小的跟踪误差和快速稳定.
结论:
- 拟议的IGWO-LQR框架为MFRH控制系统的精度和动态响应提供了显著的改进.
- 这种优化的控制策略对提高辅助机器人手的能力具有重大前景.
- IGWO算法有效调整LQR控制器,在苛刻的机器人应用中表现优于传统方法.
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