SGER: Robust Intelligent Automation for Life Science Systems

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This project studies a new integrated data-driven planning and control scheme for life science automation. Particular attention is paid for the objective of designing a highly robust and intelligent system to achieve the safety and reliability.The research here intends to (i) create a framework for facilitating analytical integration of planning and control, (ii) adopt a two degree-of-freedom controller for robust and intelligent life science automation, (iii) test and evaluate of the obtained results via simulation, (iv) evaluate the proposed glucose controller by a quantitative grading system measure, and (v) establish contact with Professor Alan Permutt at Washington University Medical School to prepare for experimental studies. The expected benefits are (i) an advanced nontrivial planning algorithm to improve safety and reliability of the design of life science system, (ii) an advanced control software for life science automation, and (iii) a science base for the development of automated life science systems. Additionally this research will significantly enhance our general understanding about nonlinear dynamics and control of a complex system and will have a specific impact on the bio-engineering fields in terms of their research and development.