An online data driven actor-critic-disturbance guidance law for missile-target interception with input constraints

In this article, we develop an online robust actor-critic-disturbance guidance law for a missile-target interception system with limited normal acceleration capability. Firstly, the missile-target engagement is formulated as a zero-sum pursuit-evasion game problem. The key is to seek the saddle point solution of the Hamilton Jacobi Isaacs (HJI) equation, which is generally intractable due to the nonlinearity of the problem. Then, based on the universal approximation capability of Neural Networks (NNs), we construct the critic NN, the actor NN and the disturbance NN, respectively. The Bellman error is adjusted by the normalized-least square method. The proposed scheme is proved to be Uniformly Ultimately Bounded (UUB) stable by Lyapunov method. Finally, the effectiveness and robustness of the developed method are illustrated through numerical simulations against different types of non-stationary targets and initial conditions.