Industrial internet model based predictive control for applications using thermal imaging

该提案的总体目标侧重于开展研究，旨在利用工业互联网平台开发智能预测控制方法。这种方法包括为大量数据分析开发计算密集型算法，以实现连续过程建模和协作机器学习。两名MSCE和两名博士生将进行这项研究。其中一些已经在接受培训，并将为长期研发和产业整合建立坚实的研究基地。对于许多先进的制造应用，建立一个精确的模型可能需要大量的计算和实验时间。拟议的研究将使用工业互联网作为快速和并行算法计算的机制，以逐步形成这些复杂的学习模型，并继续尽可能地使用所有可用的数据来精确预测过程(ES)动态。热成像数据将被第一位博士研究员用来制定这些先进的模型。结合预测分析和机器学习，该提案旨在重新制定先进的预测控制器，以提高计算效率，以便将它们整合到快速反应系统中，特别是先进制造中的机器人系统和高速运动控制。将考虑各种预测性方案，包括重新拟定的最新动态。研究将标准矩阵代数形式转换到离散空间，并将其与标准公式的计算速度进行比较。第一个MSCE学生将进行这项工作。第二个MSCe学生将开始设计各种系统的基于图像的控制，集成第一个MSCe派生的快速控制例程。该战略是制定控制方案，能够在这样的系统上提供最小的复杂设定点轨迹的跟踪误差，包括连续机器人焊接和快速运动制造过程。派生的算法将与第一个博士工作中基于互联网的初始监测程序集成在一起。第二名博士生将把上述项目的关键要素整合到她/他的研究中。这项初始任务将包括通过工业互联网将过程建模算法的元素、热图像处理例程和快速计算控制例程结合在一起。由于研究涉及到热能和成像数据的使用，因此必须制定一种新的适用于各种热过程的自适应空间预测控制律。这项工作将在各种规模的工业系统以及大型工业系统上实施。这项研究将促进这项研究的长期愿景的成功。

The overall objective of this proposal focuses on conducting research aimed at developing intelligent predictive control methodologies using the platform of the industrial internet. This approach involves developing computationally intensive algorithms for high volume data analytics to enable continuous process modelling and cooperative machine learning. Two MScEs and 2 PhD students will conduct the research. Some are already being trained and will establish a solid research base for long-term R&D and industrial integration. For many advanced manufacturing applications, formulating an accurate model can be demanding on computations and time consuming in experimentation. The proposed research will use the industrial internet as a mechanism for fast and parallel algorithmic computations that are to be derived in order to progressively formulate these complex learning models, and continue to use all available data to the extent possible for accurate predictive process(es) dynamics. Thermal imaging data will be used in formulating these advanced models by the the first PhD researcher. In conjunction with predictive analytics and machine learning, the proposal aims at reformulating advanced predictive controllers for computational efficiency in order to integrate them on fast reacting systems, specifically robotic systems and high speed motion control in advanced manufacturing. Various predictive schemes will be considered including the most recent developments for reformulation. Research on transforming standard matrix algebraic forms to discrete space will be performed and applied, as well as comparing to the standard formulations for computational speed. The first MScE student will conduct this work. The second MScE student will commence work on designing image-based control of various systems, integrating the fast control routines derived by the first MScE. The strategy is to formulate control schemes that can provide minimum tracking errors of complex setpoint trajectories on such systems, including continuous robotic welding and fast motion manufacturing processes. The derived algorithms will be integrated with initial internet-based monitoring routines from the first PhD’s work. The second PhD student will conduct the integration of key elements of the above projects into her/his research. This initial task will include combining the elements of process modelling algorithms via the industrial internet, thermal image processing routines and the fast computational control routines. As the research involves the use of thermal energy and imaging data, a new adaptive and spatial predictive control law for various thermal processes has to be formulated. The work would be implemented on various scaled industrial systems as well as large-scaled industrial systems. This research will promote the success of the research’s long-term vision.