Adaptive control of air-to-air missile using multilayer NN feedforward and RISE feedback terms

In this work, we present the application of a neural network (NN) based adaptive control scheme for the purpose of trajectory tracking of an air-to-air missile. The nonlinear dynamic model of such missiles contain numerous aerodynamic coefficients. It is well known that uncertainty in these coefficients can cause degradation of control performance. In this work, we explore the feasibility of applying a nonlinear adaptive controller employing NN as feedforward, augmented with a continuous robust integral of the signum of the error (RISE) term connected as feedback in order to improve the performance of the degraded system. Typically NN based adaptive controller guarantees only uniformly ultimately bounded stability while the suggested control system assures semi-global asymptotic tracking of the missile. In order to accomplish the adaptive behavior, the neural network is passed through learning stage to update the weight matrices and gains by using the adaptation rules that are derived from the Lyapunov stability techniques. Results of the simulations of the proposed algorithm on six degree of freedom nonlinear missile validates feasibility of the control law. The result of the proposed controller are compared with results obtained using the backstepping technique in this paper.