Design of Missile Guidance Law Using Takagi-Sugeno-Kang (TSK) Elliptic Type-2 Fuzzy Brain Imitated Neural Networks

Abstract

The exploiting of missile guidance law is one of the most important issue of defense systems. However, it is a complex nonlinear guidance control problem. In recent years, several intelligent algorithms have been employed for the missile guidance law design. This paper aims to provide a more effective intelligent neural network, and then apply it to missile guidance control. An elliptic type-2 function (ET2F) combined with a Takagi-Sugeno-Kang (TSK) fuzzy system (FS) based on a wavelet function and a brain imitated neural network (BINN) is proposed to produce a new Takagi-Sugeno-Kang elliptic type-2 fuzzy brain imitated neural network (TSKET2FBINN). The proposed TSKET2FBINN is then incorporated into the missile guidance law. The proposed missile guidance control system includes the TSKET2FBINN controller and a robust compensation controller. A Lyapunov function is used to generate parameter adjustment adaptive laws so as to guarantee system stability with fast tracking performance of missile guidance control system. By presenting three missile control scenarios, the effectiveness of the proposed method is then demonstrated. The miss-distance (MD) of the proposed method is calculated and compared to other recent guidance methods to demonstrate its superior performance.