Industrial internet model based predictive control for applications using thermal imaging

点击翻译按钮获取中文摘要

Summary of Proposal***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.**