Intelligent Adaptive Law for Missile Guidance Using Fuzzy Neural Networks

Abstract

In this paper, an adaptive fuzzy neural network (FNN) controller is proposed for missile guidance. The objective is for one defending missile (DM) to intercept an attacking missile (AM) in air battle scenario. The adaptive FNN controller is adopted to force the DM toward the AM under the existence of disturbance, and a monitoring controller is also designed to reduce the error between FNN controller and ideal controller. In comparison with the other multi-layered neural network controller, the proposed adaptive FNN controller which combines the fuzzy rules with the neural network can be easily designed. The weighting factors of our new FNN controller are activated to dispatch the DM toward the AM. Using the Lyapunov constraints, the weighting factors for the proposed FNN controller are updated to guarantee the stability of the path planning system. In our illustrated examples, the systematic battle environment is constructed. From the simulation results, the proposed adaptive FNN controller is capable of performing missile guidance, and it also shows that the computation load of our proposed approach is much less than that of using cerebellar model articulation controller (CMAC).