Neural Network-Based Controller for Terminal Guidance Applied in Short-Range Rockets

Precision during the guided terminal flight in short-range rockets is a key factor that must be improved using several methodologies. Traditionally, inertial navigation systems allowed to unbind the accuracy concerning the range. Unfortunately, in short-range rockets, the response of the inertial-based controller has to be fast enough to correct the trajectory in a short time. This fact implies that to achieve high precision, either the use of systems too large to be implemented in portable rockets is required, or systems that are too expensive concerning the total cost of the rocket. For these systems, artificial intelligence-based guidance tactics could improve the precision, since once the network is trained, it is not necessary to know the dynamics of the vehicle, and the network itself can react quickly, simplifying the system and reducing economic costs using relatively simple electronics. The neural networks are trained using a nonlinear-dynamics model based on a simulated flight dynamics and verified with real flight data. The simulation results show that the proposed method works effectively in a six-degree-of-freedom simulation environment, with excellent accuracy and robustness to parameter uncertainty. The appropriateness of the closed-loop performance is validated using Monte Carlo analysis across a wide range of uncertainty scenarios.