Application of genetic algorithms to hypersonic flight control

The paper presents an application of genetic algorithms to the design of a longitudinal flight controller for a hypersonic accelerator vehicle which is to be used to launch small satellites. A feature of hypersonic air-breathing flight vehicles is the high level of engine integration with the airframe. As a result, maintenance of vehicle attitude is not simply an issue of stability, but also one of propulsive effectiveness, which itself varies with flight conditions and the vehicle attitude. There is therefore limited scope for departure from optimum operating conditions. This, together with the extreme flight conditions, performance uncertainty, and the inherent instability of the vehicle, contributes to a demanding control task. We examine the capacity of a genetic algorithm in designing a fuzzy logic controller for the task of closed loop flight control. With a fixed, preset control structure, the design task is to configure the control surface through selection of the rule consequents and input scaling. The genetic algorithm uses a collection of simulated flight response in its formulation of the objective function. This allows the generation of a controller design without linearization of the vehicle model and dynamics. Stability augmentation is shown through flight simulation at the low-speed end of the hypersonic trajectory and also at a higher flight speed