Reduced Order Guidance Methods and Probabilistic Techniques in Addressing Mission Uncertainty

Abstract

Recognizing that vehicle synthesis fulfills the role of integrator of the mutually interacting disciplines, difficulties persist in intelligently implementing disciplinary analysis into this synthesis process. This paper develops and describes analytical and statistical approximation techniques used to create design-oriented analyses which are implementable in the process. Specifically, techniques related to the vehicle guidance discipline are examined. The ultimate goal is to investigate the economic viability of an aerospace system in the face of uncertainty at the system and discipline design levels. The notion of a requirement is replaced by a modeling of mission variability, since future aircraft will likely fly a variety of missions. Aircraft guidance laws are key components in the mission analysis portion of an aircraft sizing code, and thus they must be included in the investigation. Through the use of statistical modeling techniques, a link between mission uncertainty, optimal guidance, wing planform, and economic objectives is obtained. This linkage allows for the investigation of guidance and mission effects on such quantities as gross weight and ticket price (on a per mile basis). Further, the resulting solutions are robust since they are obtained by choosing control parameters which maximize the probability of meeting a target while simultaneously assuring that appropriate constraints (which are also probabilistic) are met.