CAREER: Data-Enabled Neural Multi-Step Predictive Control (DeMuSPc): a Learning-Based Predictive and Adaptive Control Approach for Complex Nonlinear Systems
职业:数据支持的神经多步预测控制(DeMuSPc):一种用于复杂非线性系统的基于学习的预测和自适应控制方法
基本信息
- 批准号:2338749
- 负责人:
- 金额:$ 65.52万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-09-01 至 2029-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This Faculty Early Career Development (CAREER) grant will fund research that enables new knowledge related to a data-enabled automatic control approach for complex processes which are hard to describe from first principles and are changing over time, promoting the progress of science and advancing national health and prosperity. Among all the processes with the above outlined properties, one compelling example is represented by the regulation of blood glucose in people with type 1 diabetes by means of exogenous insulin. Insulin therapy is affected by a considerable number of unknown and hidden physiological variables that change with patient’s lifestyle and growth/aging, requiring frequent interventions by patients and their caregivers. Despite intensive and burdensome treatment, the majority of patients still fail to meet their prescribed glycemic targets leading to complications which are costly to both the individual and the healthcare system. This project will support fundamental research to provide needed knowledge for the development of data-driven and learning-based predictive and adaptive automatic control. The success of this project will enable a framework for optimal regulation and adaptation to changes with application in healthcare, biomedical, advanced manufacturing, chemical or automotive industries. The research is integrated with educational and outreach activities to broaden participation of groups traditionally underrepresented in control research and contribute positively to engineering education.This research aims to make fundamental contributions to data-enabled predictive and adaptive control to overcome several limitations affecting existing predictive control approaches, including large errors in the model predictions for long prediction horizons due to large plant-model mismatch and unmodeled dynamics, as well as policy parameters that are static and do not adapt to varying operating conditions. The project will (1) exploit the use of multi-step ahead output predictors with a structure nonlinear in the state and affine in the future control moves, (2) identify the unknown mappings in the predictor parameterizations from input-output data by means of neural networks embedding prescribed behavioral guarantees in their structure, (3) integrate the predictors into a linear time-varying model predictive control framework, and (4) use Bayesian Optimization to tune and adapt the parameters of the controller to changes in the dynamics. The algorithms will be validated on the motivating examples of automated glucose regulation in people with type 1 diabetes by performing extensive in-silico trials with a metabolic simulator.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该学院早期职业发展(CAREER)补助金将资助研究,使新知识与复杂过程的数据支持自动控制方法相关,这些过程很难从第一原理描述,并随着时间的推移而变化,促进科学进步,促进国家健康和繁荣。在具有上述性质的所有过程中,一个令人信服的例子是通过外源性胰岛素调节1型糖尿病患者的血糖。胰岛素治疗受大量未知和隐藏的生理变量的影响,这些变量随着患者的生活方式和生长/衰老而变化,需要患者及其护理人员进行频繁干预。尽管进行了密集和繁重的治疗,但大多数患者仍然无法达到规定的血糖目标,导致并发症,这对个人和医疗保健系统都是昂贵的。该项目将支持基础研究,为开发数据驱动和基于学习的预测和自适应自动控制提供所需的知识。该项目的成功将为医疗保健、生物医学、先进制造、化学或汽车行业的应用提供一个最佳监管和适应变化的框架。该研究与教育和推广活动相结合,以扩大传统上在控制研究中代表性不足的群体的参与,并为工程教育做出积极贡献。该研究旨在为数据支持的预测和自适应控制做出根本性贡献,以克服影响现有预测控制方法的几个限制,包括由于大的工厂-模型失配和未建模的动态以及静态的且不适应变化的操作条件的策略参数而导致的长预测范围的模型预测中的大误差。该项目将(1)利用多步超前输出预测器的使用,其结构在状态中是非线性的,在未来的控制动作中是仿射的,(2)通过在其结构中嵌入规定的行为保证的神经网络来识别来自输入-输出数据的预测器参数化中的未知映射,(3)将预测器集成到线性时变模型预测控制框架中,以及(4)使用贝叶斯优化来调整和调整控制器的参数以适应动态变化。通过使用代谢模拟器进行广泛的计算机模拟试验,这些算法将在1型糖尿病患者自动血糖调节的激励性示例中得到验证。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Marzia Cescon其他文献
Data-enabled learning and control algorithms for intelligent glucose management: The state of the art
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:
- 作者:
Deheng Cai;Wenjing Wu;Marzia Cescon;Wei Liu;Linong Ji;Dawei Shi - 通讯作者:
Dawei Shi
Correction to: Substrate temperature estimation and control in advanced MOCVD process for superconductor manufacturing
- DOI:
10.1007/s00170-024-13778-3 - 发表时间:
2024-05-23 - 期刊:
- 影响因子:3.100
- 作者:
Amal Chebbi;Karolos Grigoriadis;Matthew Franchek;Marzia Cescon - 通讯作者:
Marzia Cescon
Marzia Cescon的其他文献
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