Prof. Ping Lu
Title: Computational Guidance and Control
Ping Lu received his baccalaureate degree from then Beijing Institute of Aeronautics, and Ph.D. degree in Aerospace Engineering from the University of Michigan. He was on the faculty of Aerospace Engineering at Iowa State University from 1990 to 2016 where his last position was Professor. He joined the San Diego State University in 2016 to be a professor and the Chair of the Aerospace Engineering Department. His research interests and expertise are in aerospace guidance, flight control, and autonomous trajectory planning and optimization. Professor Lu was the recipient of the prestigious American Institute of Aeronautics and Astronautics (AIAA) Mechanics and Control of Flight Award in 2008, “for contributions in advanced guidance algorithms for entry and ascent flight”. In 2016 he was a recipient of the NASA Johnson Space Center Director’s Innovation Group Achievement Award. Professor Lu is an AIAA Fellow, and the Editor-in-Chief of the Journal of Guidance, Control, and Dynamics.
An emerging trend in the field of aerospace guidance and control is what we call “Computational Guidance and Control” (CG&C). In CG&C, traditional guidance and control laws and controllers of fixed structures are replaced by algorithms. CG&C allows much more complex guidance and control tasks to be performed than ever before, offering great potential for significant increase in capability and performance, and reduction in recurring operational costs associated with the G&C systems. With the continued advent in onboard computational capability and customization of algorithms, CG&C is expected to become an increasingly prevalent phenomenon in Guidance and Control, and a key part of the foundational technologies for aerospace system autonomy and autonomous operations. In this presentation, we will offer our perspective on what CG&C entails, what are the characteristics of CG&C in contrast to traditional G&C and other branches of computational sciences and engineering. The requirements of onboard computational efficiency, reliability, and robustness of CG&C demand up-front investment in modeling and analyzing the problem, and novel design of the algorithm. An application in human Mars missions will be provided to demonstrate how this philosophy and CG&C can be practiced in realistic problems to significantly outperform the state-of-the-art technology.
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