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Meeting	ACGS Committee Meeting 126 - Virtual - March 2021
Agenda Location	8 SUBCOMMITTEE D – DYNAMICS, COMPUTATIONS, AND ANALYSIS 8.3 Descent Guidance with State Constraints via a Dual Quaternion Formulation
Title	Descent Guidance with State Constraints via a Dual Quaternion Formulation
Presenter	Behcet Acikmese
Affiliation	University of Washington
Available Downloads*	presentation
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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 spacecraft, 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 convexification, that is, the conversion of the resulting optimization problems into convex optimization problems, for which we can assure obtaining numerical solutions in real-time. 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 spacecraft 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. The underlying trajectory planning problems for planetary landing problems are nonconvex, due to complex state, control, and coupled state and control constraints. We have been developing methods of convexification to handle these sources of complexity. We developed a novel "lossless convexification" method to handle nonconvex control constraints, which has shown to be enabling for the next generation Mars robotic sample return and manned missions. Building on this breakthrough, we also developed a method called "successive convexification" to handle a general class of nonconvex state and coupled state and control constraints encountered in Moon and Earth landings. We will also present efficient first-order methods of convex optimization, which exploit the structure of the resulting trajectory planning problems.