Education
- PhD, Aerospace Engineering, Purdue University
- MS, Mechanical Engineering, Purdue University
- BS, Civil Engineering, Middle East Technical University (Ankara, Turkey)
Bio
Dr. Açıkmeşe received his M.S. in Mechanical Engineering and his Ph.D. in Aerospace Engineering from Purdue University.
He was a technologist and a senior member of the Guidance and Control (G&C) Analysis Group at NASA Jet Propulsion Laboratory (JPL) from 2003 to 2012 and was a visiting Assistant Professor of Aerospace Engineering at Purdue University before joining JPL.
At JPL, he developed guidance, control, and estimation algorithms for formation-flying spacecraft and distributed networked systems, proximity operations around asteroids and comets, and planetary landing, as well as developing interior point methods algorithms for the real-time solution of convex optimization problems. Dr. Açıkmeşe’s research developed a fundamental result, known as “lossless convexification”, that provides the solution of a general class of nonconvex optimal control problems via computationally tractable convex optimization methods. This theoretical insight led to a leap in the G&C technology that now made planetary pinpoint landing feasible. NASA has been investing on the demonstration of this technology to mature it for next generation missions to Mars and other planets.
Dr. Açıkmeşe also worked on NASA missions. He was a member of NASA’s Mars Science Laboratory (MSL) G&C team, where he developed and delivered G&C algorithms used in the “fly-away phase” of the successful Curiosity rover landing in August 2012. He also developed Reaction Control System (RCS) algorithms for NASA’s SMAP (Soil Moisture Active Passive) mission, which will be launched in 2014.
Research Interests
- Convex Optimization And Its Engineering Applications
- Guidance, Navigation, And Control (GN&C) Of Autonomous Vehicles
- Model Predictive Control (MPC)
- Nonlinear And Robust Control
- Distributed Control Systems
Publications
Awards
- University of Washington Faculty Appreciation for Career Education & Training (FACET) Celebration, for a faculty member that has positively impacted their career and professional development, 2019.
- Best paper award for 2015, Asian Journal of Control, for “Markov chain approach to probabilistic guidance for swarms of autonomous agents”.
- NSF CAREER Award for “Real-time Convex Optimization for High-Performance Control of Autonomous Systems,” 2015
- NASA Group Achievement Award for “Successfully demonstrating real-time terrain relative navigation and fuel-optimal trajectory planning on Masten’s Xombie rocket vehicle,” 2015.
- NASA Group Achievement Award for “Exceptional performance in two successful test flights on Masten’s rocket vehicle, demonstrating in-flight fuel-optimal trajectory planning and execution capabilities,” 2014
- AIAA Associate Fellow, 2014.
- NASA Group Achievement Award for “Exceptional performance in the design, development, fabrication, test, and operation of the Mars Science Laboratory GN&C system,” 2013.
- AFRL Summer Faculty Fellowship, 2013 and 2014.
- JPL Summer Faculty Award, 2013.
- Purdue University President Council Guest of Honor for his key role in NASA MSL mission.
- IEEE Senior Member, 2009.
- NASA Group Achievement Award for “Outstanding development and ground demonstration of guidance and control technologies for spacecraft formation flying,” 2009.
- JPL Mars Science Laboratory Team Bonus Award for “Successfully developing a highly innovative Entry, Descent, and Landing strategy for MSL,” 2008.
- JPL Mariner Award for individual performance, 2005, 2008, 2009.
- JPL Team Award for Formation Flying Technology, 2006.
- NASA Team Bonus Award for Formation Control Testbed, 2004.
- JPL Spot Award for research on the stability of formation flying spacecraft, 2004.
- Purdue Research Foundation graduate research fellowship, 1999-2000.
- Graduate fellowship from Turkish Ministry of Education, 1994-1996
- Graduate fellowship from The Scientific and Technological Research Council of Turkey, 1993.
- Over 15 NASA Tech Brief Awards.