Optimization-Based Control of Autonomous Aerospace Systems

Overview

Abstract

Many future aerospace engineering applications will require dramatic increases in our ex- isting 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 per- formance 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 au- tonomous control problems as numerically tractable optimization problems. The key idea is convexification, 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. Exploiting convexity enables i) reliable onboard computations; ii) full utiliza- tion 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 impor- tant 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 convexification” method to solve this problem, which enables the next genera- tion planetary missions, such as Mars robotic sample return and manned missions. We will also present a method called “successive convexification” to handle a general class of control problems, including aerial drone, autonomous rendezvous and docking, and rocket landing trajectory planning. We will also present efficient first-order methods of convex optimization, which exploit the structure of trajectory planning problems. 

Brief Biography

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 used successfully in NASA’s Mars Science Laboratory (MSL) and Mars 2020 missions 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.