Abstract:
Many future aerospace engineering applications will require dramatic increases in our existing autonomous control capabilities. These include robotic sample return missions to planets, comets, and asteroids, formation flying spacecraft, swarms of autonomous agents, unmanned aerial, ground, and underwater vehicles, and autonomous commercial robotic applications. A key control challenge for many autonomous systems is to achieve the performance goals safely with minimal resource use in the presence of mission constraints and uncertainties. In principle these problems can be formulated and solved as optimization problems. The challenge is solving them reliably in real-time.

Our research has provided new analytical results enabling the formulation of many autonomous control problems as numerically tractable optimization problems. The key idea is convexication, that is, the conversion of the resulting optimization problems into con-vex optimization problems, for which we can assure obtaining numerical solutions in real-time. The mathematical theory behind convexication is the duality theory of optimization, which manifests itself as Pontryagin’s Maximum Principle in infinite-dimensional problems and as KKT conditions in nite-dimensional problems. Exploiting convexity enables i) reliable onboard computations; ii) full utilization of the performance envelope for the autonomous system; iii) systematic verification of the control algorithms.

This seminar introduces several real-world aerospace applications, where this approach provided dramatic performance improvements over the heritage technologies. An important application is the fuel optimal planetary soft landing, whose complete solution has been an open problem since the Apollo Moon landings of the 1960s. We developed a novel “lossless convexication” method to solve this problem, which enables the next generation planetary missions, such as Mars robotic sample return and manned missions. We will also present a method called “successive convexication” to handle a very general class of control problems, such as aerial drone and rocket landing trajectory planning. Another application is in Markov chain synthesis with “safety” constraints, which has enabled the development of new decentralized coordination and control methods for vehicle swarms.

Biosketch:
Behcet Acikmese is a professor in the William E. Boeing Department of Aeronautics and Astronautics and an adjunct faculty member in the Department of Electrical Engineering at University of Washington, Seattle. He received his Ph.D. in Aerospace Engineering from Purdue University. He was a senior technologist at JPL and a lecturer at Caltech. At JPL, he developed control algorithms for planetary landing, spacecraft formation flying, and asteroid and comet sample return missions. He developed the flyaway” control algorithms in Mars Science Laboratory (MSL) mission and the RCS algorithms for NASA SMAP mission. Dr. Acikmese invented a novel real-time convex optimization based planetary landing guidance algorithm (G-FOLD) that was flight tested by JPL, which is the first demonstration of a real-time optimization algorithm for rocket guidance. He is a recipient of the NSF CAREER Award, IEEE Technical Excellence in Aerospace Controls, numerous NASA Achievement awards for his contributions to NASA missions and technology development. He is an Associate Fellow of AIAA, a Senior Member of IEEE, and an associate editor of IEEE Control System Magazine and AIAA JGCD.