Design of a distal teacher recursive estimator for airplane flight controllers

Aircraft controllers in general have particular flight limitations in which the aircraft is controllable. It may be difficult, if not impossible to control an aircraft using a classical controller if the dynamics of the aircraft were to change or if the flight limitations are exceeded. This is because classical controllers for airplanes are designed according to specific dynamics of the system. This paper addresses the design of an estimation system for the purpose of aircraft control using artificial intelligence techniques. The method proposed for the design of the estimation system is the distal teacher recursive estimator. Intelligent decision-making techniques have emerged to overcome some of the deficiencies in conventional techniques when dealing with complex systems. These problems include knowledge adaptation, learning, and expert knowledge incorporation. Both neural networks and fuzzy systems have unique advantages. Neural networks have the advantage in learning while fuzzy systems have the advantage in inferencing. Neural networks and fuzzy systems have been used to implement intelligent failure detection and accommodation systems that should be able to learn and take actions in a way similar to humans. It should also be able to incorporate fuzziness and imprecision which exist in real-world systems.