Coordinated Supervisory Control System for Smart Manufacturing

该项目将支持智能制造工艺和系统的基础研究，促进科学进步和国家繁荣。传感器、信息、通信和机器人技术的最新进展推动了更好的制造方式。现代制造系统配备了多个传感器、通信设备和物料搬运机器人，这些机器人与其他自主机器和人类同事/主管一起工作。然而，这些技术进步在提高生产效率和质量方面的全部潜力尚未实现。这是因为制造系统本质上是随机和非线性的，并且缺乏对制造性能和生产控制的实时基于模型的预测的理论和技术理解。这项研究将建立新型的数据支持模型，用于预测不确定性下的制造系统性能，以及用于生产控制的自适应、鲁棒性和可扩展的控制和协调算法。新知识将为制造商提供严格的定量工具，用于实时监测和控制复杂的制造系统。这项研究的结果将有助于智能制造的理论和实践，并将对整个美国制造业非常有用，以最大限度地提高生产力和经济效益。这项研究将目标与工业需求相结合，有助于扩大代表性不足的群体在研究中的参与，并加强工程教育。该研究将通过对生产动态和多机器人物料调度决策的集成研究来填补知识空白，以实时解决它们的相互作用，这对制造系统的效率具有重要意义。研究方法将是一种新的组合动态系统建模的材料流通过生产过程沿着动态多机器人任务分配下的不确定性，优化生产性能指标。该研究小组旨在：a）建立一个数据驱动的数学框架来描述具有动态生产和多机器人操作的实时制造系统，以增强对动态制造过程和系统的理解;和B）为机器/人协同工作的多机器人系统建立自适应控制和分散决策的科学和技术基础。协调监控系统基于物理系统分析、高级数据驱动建模和自适应控制的整体视图。该方法可转换为其他动态系统与分布式传感器和数据，如运输，供应链和医疗保健管理。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

This project will support fundamental research in smart manufacturing processes and systems, promoting the progress of science as well as national prosperity. Recent advancements in sensors, information, communication, and robotics technologies have driven better ways of doing manufacturing. Modern manufacturing systems are equipped with multiple sensors, communication devices, and material handling robots that operate alongside other autonomous machines and human coworkers/supervisors. However, the full potential of these technological advances in improving production efficiency and quality has not been realized. This is because manufacturing systems are inherently stochastic and nonlinear, and there is a lack of theoretical and technical understanding of real-time model-based prediction of manufacturing performance and production control. This research will establish novel data-enabled models for predicting manufacturing system performance under uncertainty and adaptive, robust, and scalable control and coordination algorithms for production control. The new knowledge will provide manufacturers with a rigorous quantitative tool for real-time monitoring and control of the complex manufacturing systems. The results of this research will contribute to both the theory and the practice of smart manufacturing and will be very useful to the entire U.S. manufacturing sector for maximizing productivity and economic benefit. This research incorporates goals with industrial needs, helps broaden the participation of underrepresented groups in research, and enhances engineering education. This research will fill the knowledge gap via the integrated study of production dynamics and multiple robots material dispatching decisions to address their interactions in real-time, which is significant for manufacturing systems efficiency. The research approach will be a novel combination of dynamic systems modeling of material flow through production processes along with dynamic multirobot task assignment under uncertainty for optimizing production performance metrics. The research team aims to: a) establish a data-enabled mathematical framework to describe real-time manufacturing systems with dynamic production and multirobot operations to enhance the understanding of dynamic manufacturing processes and systems; and b) establish the scientific and technological foundation in adaptive control and decentralized decision making for multirobot system working with machines/humans. The Coordinated Supervisory Control System is based on the holistic view of physical system analysis, advanced data-driven modeling, and adaptive control. The methodology is transformable to other dynamic systems with distributed sensors and data, such as transportation, supply chain, and health care management.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.