Adaptive Integrated Guidance and Control Design for a Missile With Input Constraints

Adaptive neural network (NN) based integrated guidance and control (IGC) is investigated for a missile with input constraints. In order to avoid the explosion of complexity in traditional design, dynamic surface control combined with backstepping are utilized to developed the IGC law. By utilizing Lyapunov synthesis, the closed-loop control signals are proved to be semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of zero by appropriately choosing design constants. Simulation results are presented to show the effectiveness of the proposed scheme.