如何不使联合扩展卡尔曼波器在非结构化机械模型中失败
Cristovão Freitas Iglesias1, Miodrag Bolic1
1School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Sensors (Basel, Switzerland)
|January 26, 2024
概括
一种新方法,SANTO,通过使用扩展卡尔曼波器 (JEKF) 实现非结构化机械模型 (UMM) 中状态和参数的联合估计,从而改善实时生物过程监测. 这克服了JEKF在复杂模型中的故障,提高了估计准确度.
科学领域:
- 生物技术是生物技术.
- 工艺工程的过程工程.
- 控制系统 控制系统
背景情况:
- 非结构化机械模型 (UMM) 对生物制造至关重要,即使没有已知的机制,也可以进行过程监控.
- 使用扩展卡尔曼波器 (JEKF) 共同估计状态和参数,对于实时生物过程监测至关重要.
研究的目的:
- 为了正式描述和证明JEKF的失败案例,当应用到UMM与未共享的参数和有限的测量.
- 提出一种新的方法,SANTO,以克服这种JEKF故障,并使同时估计成为可能.
主要方法:
- 在特定的UMM配置中,JEKF故障的正式描述和数学证明.
- 开发了SANTO方法,修改了初始状态误差共变矩阵P(t=0).
- 使用合成和真实生物制造数据集进行实证评估.
主要成果:
- 由于对非共享参数的恒定零卡尔曼增益,证明了JEKF故障.
- SANTO成功地通过调整P(t=0) 来阻止卡尔曼增益变为零.
- 与经典JEKF相比,实现了高达17%的平均平方根百分比误差 (RMSPE) 的降低.
结论:
- SANTO方法有效地解决了JEKF在UMM中未共享参数的故障.
- SANTO显著提高了实时生物过程监测的准确性.
- 这项工作为估计复杂生物制造系统中的状态和参数提供了可靠的解决方案.
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