Determining the Consistency factor of Autopilot using Rough Set Theory
This work addresses the need for reliable autopilot evaluation in aviation to ensure safety, but it appears incremental as it applies an existing method (Rough Set Theory) to a specific domain without claiming broad breakthroughs.
The paper tackled the problem of evaluating the consistency and reliability of autopilot systems in aircraft, which is difficult due to complex and imprecise data, by proposing an approach using Rough Set Theory to determine a Consistency Factor from grouped factors, resulting in identification of the most and least influential factors for autopilot performance.
Autopilot is a system designed to guide a vehicle without aid. Due to increase in flight hours and complexity of modern day flight it has become imperative to equip the aircrafts with autopilot. Thus reliability and consistency of an Autopilot system becomes a crucial role in a flight. But the increased complexity and demand for better accuracy has made the process of evaluating the autopilot for consistency a difficult process .A vast amount of imprecise data has been involved. Rough sets can be a potent tool for such kind of Applications containing vague data. This paper proposes an approach towards Consistency factor determination using Rough Set Theory. The seventeen basic factors, that are crucial in determining the consistency of an Autopilot system, are grouped into five Payloads based on their functionality. Consistency Factor is evaluated through these payloads, using Rough Set Theory. Consistency Factor determines the consistency and reliability of an autopilot system and the conditions under which manual override becomes imperative. Using Rough set Theory the most and the least influential factors towards Autopilot system are also determined.