具有控制的自适应数字双胞胎建模:集成基于扩展卡尔曼波器的递归短线非线性识别与模型预测控制
Jingyi Wang1, Liang Cao2, Yankai Cao1
1Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
本研究介绍了一个数字双胞胎框架,以克服工业过程模拟的挑战. 新方法减少了开发时间,提高了准确性,提高了控制效率.
科学领域:
- 工业过程控制 工业过程控制
- 数字双胞胎技术的数字双胞胎技术
- 系统动力学系统动力学
背景情况:
- 数字双胞胎为工业过程模拟,监控和控制提供了巨大的潜力.
- 目前的实施面临诸如长时间开发,模型精度降低和互动性有限等挑战.
研究的目的:
- 提出一个全面的数字双胞胎发展框架,解决关键的实施挑战.
- 提高数字双胞胎在工业过程中的有效性,准确性和互动性.
主要方法:
- 开发了一个整合数字双胞胎识别,实时模型更新和先进过程控制的框架.
- 利用非线性动态的稀疏识别用于离线模型识别,减少开发时间.
- 采用扩展的卡尔曼波器进行实时模型准确度减轻.
- 将更新的模型集成到模型预测控制中,以优化控制输入.
主要成果:
- 证明了在保持模型忠实性的同时,减少了数字双胞胎开发时间.
- 通过实时更新,成功地减轻了模型精度的下降.
- 增强控制输入优化和数字双胞胎互动性.
- 通过工业案例研究和模拟示例验证了框架的优势.
结论:
- 拟议的数字双胞胎框架有效地解决了工业应用中的关键挑战.
- 稀疏识别,扩展的卡尔曼过器和模型预测控制的整合提供了一个强大的解决方案.
- 该方法提高了数字双胞胎在工业环境中的实际实用性和性能.
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