CAREER: Data-Enabled Neural Multi-Step Predictive Control (DeMuSPc): a Learning-Based Predictive and Adaptive Control Approach for Complex Nonlinear Systems

这笔学院早期职业发展(Career)补助金将资助研究，使与数据启用的自动控制方法相关的新知识能够用于复杂的过程，这些过程从最初的原则很难描述，并且随着时间的推移而变化，促进科学的进步，促进国家的健康和繁荣。在具有上述特性的所有过程中，一个引人注目的例子是通过外源性胰岛素调节1型糖尿病患者的血糖。胰岛素治疗受到大量未知和隐藏的生理变量的影响，这些变量会随着患者的生活方式和生长/衰老而变化，需要患者和他们的照顾者经常干预。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他们规定的血糖目标，从而导致并发症，这对个人和医疗系统都是昂贵的。该项目将支持基础研究，为数据驱动和基于学习的预测和自适应自动控制的发展提供必要的知识。该项目的成功将为医疗保健、生物医学、先进制造、化工或汽车行业的应用提供一个最佳监管和适应变化的框架。这项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较低的群体的参与，并为工程教育做出积极贡献。本研究旨在为数据使能预测和自适应控制做出基本贡献，以克服影响现有预测控制方法的几个限制，包括由于大的对象模型失配和未建模的动态导致对长期预测周期的模型预测存在较大误差，以及策略参数是静态的且不适应变化的操作条件。该项目将(1)利用具有状态和仿射结构的多步超前输出预测器，(2)通过在其结构中嵌入规定的行为保证的神经网络从输入-输出数据中识别预测器参数中的未知映射，(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适应控制器的参数以适应动态变化。这些算法将通过使用代谢模拟器进行广泛的计算机内试验，在1型糖尿病患者中进行自动化血糖调节的激励实例上进行验证。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.