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Real-Time Optimization

Task Manager

Dr. Behcet Acikmese (Formation Control and Estimation)

Dr. A. Behcet AcikmeseDr. Behcet Acikmese is a senior member of the Guidance and Control Analysis Group at Jet Propulsion Laboratory (JPL). He received his M.S. in Mechanical Engineering, and his PhD in Aerospace Engineering, both degrees from Purdue University. He was a Visiting Assistant Professor of Aerospace Engineering at Purdue University before joining JPL. His research interests include robust and nonlinear control theory, optimal control, model predictive control (MPC), convex optimization and linear matrix inequalities (LMIs) in guidance, control and estimation, and real-time optimization.

He is currently developing guidance, control, and estimation algorithms for formation flying spacecraft and distributed networked systems, proximity operations around asteroids and comets, and Mars and the Moon landing, as well as developing and implementing interior point methods and multiparametric programming algorithms for the onboard real-time numerical solution of convex programming problems. Dr. Acikmese’s current research developed a fundamental result, known as “lossless convexification,” that provides the solution of a general class of nonconvex optimal control problems via convex optimization methods instead of intractable nonconvex optimization techniques. This discovery has enabled a number of key technological innovations at JPL in the area of powered descent guidance for planetary landing.

Team Member

Dr. Lars Blackmore

Lars BlackmoreLars Blackmore is currently a member of the Guidance and Control Analysis group at JPL. His work focuses on guidance and control for planetary pinpoint and precision landing, guidance and control for comet and asteroid sample return, guidance and control under stochastic uncertainty, and estimation for formation-flying spacecraft. Dr. Blackmore's research has provided algorithms for "chance-constrained" guidance and control, which guarantee a prescribed probability of mission success despite uncertainty in the form of estimation error, modeling error, and disturbances. In addition, Dr. Blackmore's current research has provided a class of "lossless convexification" results, which enable nonconvex powered descent guidance problems to be solved using tractable convex optimization techniques. Dr. Blackmore's ongoing work in multiparametric programming enables spacecraft guidance and control problems to be solved almost-optimally onboard without performing iterative optimization.

Dr. Blackmore received his PhD in Control and Estimation in 2007 from the Massachusetts Institute of Technology, and has an MEng in Electrical Engineering from the University of Cambridge (UK). Lars' previous research experience includes projects with the McLaren Formula One Racing Team, the Monterey Bay Aquarium Research Institute, the JPL Advanced Robotic Controls Group and Airbus.

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