Genetic Algorithm Based Constrained Control Allocation for Tailless Fighter

Abstract

A genetic algorithm based optimization approach is proposed for constrained control allocation problem via adjusting the components of output vector and finding a proper vector in the attainable moment set (AMS) autonomously. The basic idea is to minimize the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> norm of error between the desired moment and attainable moment using the designing freedom provided by redundant control surfaces. With the constraints of control surfaces, in order to obtain desired performance of aircraft such as stability and maneuverability, the weights of different components are updated by the genetic algorithm, which makes the close-loop system self-adaptation. As a demonstration, application of the proposed approach to the designing of control system for a tailless fighter is discussed. The results show good closed loop performance and validate the proposed intelligent optimization approach of constrained control allocation for flight control