Nonlinear Differential Game Trajectory Generation Algorithm via Adaptive Dynamic Programming

In this paper, a computational intelligent trajectory generation algorithm is proposed based on adaptive dynamic programming. The terminal homing guidance problem is first transformed to a nonlinear differential game problem, since it is hard to obtain an analytical solution for the Hamilton-Jacobi-Bellman (HJB) equation, an online guidance scheme is established by utilizing the policy iteration (PI) algorithm. The two procedures of evaluation and improvement are implemented by constructing actor-critic neural network (NN), and the approximate optimal solution can be obtained iteratively. On account of the weights of given network are updated by online data sampling, explicit information of the system internal dynamics is not needed. Finally, by applying this method to the terminal guidance scenario, numerical simulations demonstrate the effectiveness of the proposed law.