使用遗传算法优化实际PID控制步行四足软机器人使用遗传算法
Hongjun Meng1, Shupeng Zhang2, Wei Zhang2
1School of Automation and Software, Shanxi University, Taiyuan, Shanxi, 030006, USA. menghj@sxu.edu.cn.
Scientific reports
|October 30, 2024
概括
一个新的基因算法优化的PID控制器增强了软机器人运动. 与传统调技术相比,这种方法显著改善了四足软机器人的步行速度和轨迹跟踪.
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
- 人工智能的人工智能
背景情况:
- 为软机器人开发有效的控制模型和控制器提出了重大工程挑战.
- 软机器人移动现有的方法往往缺乏精度和效率.
研究的目的:
- 提出并验证使用基因算法优化的PID控制器为四足软机器人提供新的行走控制方法.
- 通过自动控制器参数调节来增强软机器人运动的性能.
主要方法:
- 使用几何分析和新型传感器特征构建了一个控制模型,将门电压与腿部曲相关联.
- 应用了一种遗传算法,自动调整参数并优化比例-整数-导数 (PID) 控制器.
- 在一个真正的3D打印的四足软机器人上演示了该方法的应用,用于步行控制.
主要成果:
- 与齐格勒-尼科尔斯调方法相比,基因算法优化的PID控制器显著改善了轨迹跟踪.
- 实现了步行速度从5mm/s增加到8mm/s,减少了2.4064%的错误率,减少了12.55%的超速,并缩短了0.5秒的响应时间.
- 通过进一步降低0.4079%的错误率,超过8.4%的超越率和1.0秒的响应时间,超越了粒子群优化.
结论:
- 拟议的基因算法优化的PID方法为控制四足软机器人运动提供了一种卓越的方法.
- 这种优化技术大大提高了控制器的性能,从而实现了更快,更准确和更稳定的机器人运动.
- 这项研究表明,对于软机器人技术的一个关键挑战,这是一种实用且有效的解决方案.
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