通过一种新的预分配时间绩效方法来对MMCPS进行有限时间最佳控制
Yilin Chen1, Yingnan Pan2, Zhechen Zhu1
1College of Control Science and Engineering, Bohai University, Jinzhou 121013, Liaoning, China.
概括
这项研究优化了使用强化学习的宏微复合物定位阶段 (MMCPS),以实现更快,更稳定的控制. 这种新方法可以在一定的时间内确保精确的定位和减轻振动,这对于合作组件工作至关重要.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 机械电子工程 机械电子工程
- 应用物理 应用物理
背景情况:
- 宏微复合物定位阶段 (MMCPS) 对于精密工程至关重要.
- 现有的控制方案缺乏对合动态的保证有限时间趋同.
- 优化宏观和微观组件之间的合作控制仍然是一个挑战.
研究的目的:
- 在MMCPS中解决有限时间最佳稳定问题.
- 制定一个控制策略,保证定位错误和合效应的收时间.
- 为了提高语音线圈电机 (VCM) 推进和压电驱动器振动减振的控制器性能.
主要方法:
- 使用牛顿第二定律建立MMCPS的动态模型.
- 通过演员关键神经网络实施强化学习策略.
- 设计一种用于位移控制的新型预分配时间性能函数.
主要成果:
- 在特定范围内实现系统错误和合效应的有限时间收.
- 优化控制器性能,确保推进和减振力量.
- 保证在预先指定的时间内,移位仅限于预先指定的区域,减少振动振幅.
结论:
- 开发的控制算法确保了所有MMCPS信号的半全球实用有限时间稳定性.
- 模拟结果验证了拟议的控制策略的可行性和有效性.
- 该方法在定位阶段加强了宏观和微观组件之间的合作工作.
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