关于量子化代学习控制的一般线性编码-解码对
IEEE transactions on cybernetics
|November 25, 2025
概括
本研究介绍了在网络约束下量子化代学习控制 (ILC) 的框架. 它为选择编码解码参数提供了指导方针,以尽量减少错误并确保控制系统的稳定性.
科学领域:
- 控制系统工程 控制系统工程
- 网络控制系统 网络控制系统
- 信号处理 信号处理
背景情况:
- 代学习控制 (ILC) 面临在网络系统中带宽和量子化有限的挑战.
- 现有的量子化ILC编码解码方案缺乏统一的框架.
- 数据速率和量化约束显著影响ILC性能.
研究的目的:
- 在道约束下,设计一个线性编码-解码对在量子化ILC中的一般框架.
- 制定系统的指导方针,用于这些对的参数选择.
- 为了最大限度地减少跟踪错误,并确保量子化ILC系统的稳定性.
主要方法:
- 开发了一个统一的数学框架,整合了现有的编码解码方案.
- 通过使用一般线性编码解码对和p型控制器,为量化ILC推导出了融合标准.
- 引入了一个控制信号忠实度指标 (CSFM) 来量化信号差异.
- 建立了有限级量化器的实际选择规则.
主要成果:
- 在量子化ILC中建立了线性编码-解码对的统一框架.
- 收标准和参数选择的系统指导方针得到了推导.
- 对于有限级量化器的实用规则可以最大限度地减少稳定状态误差和没有和的CSFM.
- 在工业机器人关节模型上的模拟验证了理论发现.
结论:
- 拟议的框架有效地解决了ILC中的量化和道限制.
- 开发的指南有助于实际选择编码解码参数,以提高控制性能.
- 这项研究提高了ILC在资源有限的网络环境中的应用性.
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