| dc.description.abstract | Electric propulsion is defining the space travel of this era with its more payload to fuel efficiency
and ability to result in robust deep space missions. Moreover, the complex sub-systems of these low thrust engines (like DAWN’s NSTAR, NEXT, Psyche’s SPT-140) provide unprecedented system-level challenges for co-optimization of trajectory and spacecraft for a holistic optimized mission. This work describes novel extensions of classical indirect methods to optimize such systems involving inequality constraints, discontinuities in states and controls and abrupt time triggered events. Furthermore, innovative methods are introduced that enable multiple preliminary trade-off aspects like mission objectives, propulsion constraints, solar power sub-systems and parameters, trajectory design and operational constraints. These challenges are addressed by an ingenious inclusion of spacecraft system level optimization in the preliminary mission design phase. The result is an indirect multi-disciplinary optimization (MDO) family of methods for missions. The approach is a fusion of invariant embedding, and mixed integer nonlinear programming with calculus of variation that very significantly expands the current class of trajectory optimization problems solvable by classical methods. The algorithms enjoy local optimality guaranteed by indirect methods though hybrid methods are employed to find the global optimal when multiple local optimal solutions are suspected. For autonomous guidance, there are many considerations like error in dynamics, bias in the sensors, actuator errors, sudden actuator failure, science operation constraints, and orbit determination requirements, that are required to be accommodated. This work introduces an original stochastic, covariance constrained guidance approach for tracking with associated contingencies for space missions. The designed algorithm achieves a desired time varying error covariance bound relative to tracking the optimized nominal trajectory by adaptively tuning a feedback controller. This contribution is anticipated to be the initiating step towards an autonomous guidance approach that enables cooperative autonomy, reliability and precision of future missions. The presented methods yield breakthrough recipes for system-level optimization involving realistic discrete operational constraints/events/multi-mode actuators with an attribute of real-time re-planning capability. The optimization approach while demonstrated on aerospace dynamical systems has a wide applicability. | |
