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

• 批准号: 2338749
• 负责人: Marzia Cescon
• 金额: $ 65.52万
• 依托单位: University of Houston
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338749&HistoricalAwards=false
• 关键词: CAREER; Data; Enabled; Neural; Multi

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.

这项教师的早期职业发展（职业）赠款将资助研究研究，以实现与基于数据的自动控制方法相关的复杂过程的新知识，这些方法很难从第一原则中描述，并且随着时间的推移而变化，从而促进了科学的进步并促进了国家健康和繁荣。在具有上述特性的所有过程中，通过极端胰岛素对1型糖尿病患者的血糖调节来表示一个令人信服的例子。胰岛素治疗受患者的生活方式和生长/衰老变化的考虑数量的考虑，这些变量需要患者及其护理人员经常干预。尽管进行了深入和抛光的治疗，但大多数患者仍然无法满足其处方的血糖靶标，导致并发症对个人和医疗保健系统都是昂贵的并发症。该项目将支持基础研究，以提供所需的知识，以开发数据驱动和基于学习的预测性和适应性自动控制。该项目的成功将实现一个框架，以适应医疗保健，生物医学，高级制造，化学或汽车行业的应用变化的最佳法规和适应性。这项研究与教育和外展活动融合在一起，以扩大传统上代表性不足的群体的参与，并为工程教育做出了积极的贡献。本研究旨在为具有数据支持的预测和适应性控制做出基本贡献，以克服几种限制，以影响现有的预测性控制方法，包括大型预测的型号，包括大型预测型的策略和预测，以及在预测的范围内，以及在型号的预测中，适用于型号，并适用于型号的型号。静态且不适应不同操作条件的参数。该项目将（1）通过在状态中非线性的结构非线性和未来控制中的仿射来利用多步进的输出预测变量，（2）通过将预测变量参数中的未知映射从输入输出数据中识别出通过嵌入到其结构中的Optimitian Prescontian Presentian Pressive the Provivation的局部预测范围，（3）的预测范围（3）（3）调整和调整控制器的参数以更改动力学。算法将在1型糖尿病患者的自动化葡萄糖调节的激励示例中得到验证，并通过代谢模拟器进行广泛的内利科试验。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛的影响来评估NSF的法定任务，并被认为是珍贵的支持。