Research On Transonic Flight Control Method

Transonic flight control is different from normal flight control. It becomes very difficult to control within transonic because of the special aerodynamics and its characteristic. Opening out the mechanism of aerodynamics and flight dynamics in transonic flight, and seeking the best control technology are the main tasks in my researches. Spring up and development of intelligent control technologies make up the deficiency of traditional control theory and control technologies, and they have powerful predominance. Some studies in transonic flight and applications of intelligent control technology are carried on in this paper. The main researches are summarized as follows.The mechanism of aerodynamics and flight dynamics in transonic flight is analyzed. Description of unsteady aerodynamics characteristic and its particularity is discussed. Based on flight dynamics mathematic model, a simulation model in transonic flight is constructed.According to the characteristic of pitching moment in transonic flight condition, a pitching moment coefficient model is reconstructed using linear model and probability function model, and a revised genetic algorithm is developed and applied in aerodynamic parameter estimation.With the advantage of fuzzy control technology and its successful applications in nonlinear and unsteady system, fuzzy controllers are designed using many variable universe stable adaptive fuzzy control technologies and plentiful simulations are done in transonic flight control. These control methods are compared and conclusions are presented.With the advantage that fuzzy neural network control (FNNC) combines fuzzy control's easiness in expressing knowledge and neural control's power in self-studying, a fuzzy neural controller is designed. Based on these, the controller is optimized using genetic algorithm.Airplane simulation model is controlled by using variable universe stable fuzzy control technology and fuzzy neural network control technology in transonic flight. Simulation results show that the designed controllers have satisfactory results in response and robust stability and show that the intelligent flight control system has good performance and potential application ability for the design of flight control system.