عنوان مقاله [English]
Advances in fighter aircraft technology have created new challenges for designers of anti air weapons systems and the need for agile missiles. The dynamics of agile missile flying are intrinsically nonlinear and may vary rapidly with time. Furthermore, these dynamics are highly uncertain, since aerodynamic data for vehicles operating under such onditions are difficult to obtain or simulate, and may in fact be a poor approximation of the actual dynamics. These and other worries have prompted researchers to look beyond classical methods, which have historically dominated the field of missile autopilot design, to robust, nonlinear, and intelligent control techinques. Most nonlinear control techniques are based on linearizing the equations of motion by the application of nonlinear feedback in trim points. Known variously as feedback linearization, dynamic inversion or gain scheduling methods, they depend heavily on knowledge of plan dynamics. More recently, neural networks have appeared as means of clearly accounting for uncertainties in plant dynamics.This paper describes a hybrid approach to the problem of STT missile control in front of unknown nonlinearities and unmodeled dynamics. The mentioned controller has been designed with the help of neural networks and dynamic inversion methods. The dynamic inversion is a derivate of the feedback linearization technique, which has been one of the most popular methods during recent decades. The number of feedbacks in the Dynamic Inversion method is determined by the order of inputs. First, the controller is designed based on the dynamic inversion method with a single feedback loop and a double one. Then, the neural network with a single hidden layer is added to the dynamic inversion controller with a single feedback loop. Finally, it is applied to STT missile air defense via two stages. The six degree of freedom (6DOF) simulation shows that neural networks adaptively can cancel the linearization errors of the approximate dynamic inversion controller by a simple weight update rule derived from the Lyapunov theory. Using the Lyapunov theory guarantees the stability of the closed-loop system.In addition, results compare the above controllers and indicate that the performance and tracking error of the neural-adaptive nonlinear autopilot is
better than other designed dynamic inversion controllers; and dynamic inversion with a double feedback loop is better than a single one.