Neural network-based optimal control for trajectory tracking of a helicopter UAV

Helicopter unmanned aerial vehicles (UAVs) may be widely used for both military and civilian operations. Because these helicopters are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper presents an optimal controller design for trajectory tracking of a helicopter UAV using a neural network (NN). The state-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman (HJB) equation in continuous time and calculates the corresponding optimal control input to minimize the HJB equation forward-in-time. Optimal tracking is accomplished with a single NN utilized for cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis, with the position, orientation, angular and translational velocity tracking errors, and NN weight estimation errors uniformly ultimately bounded (UUB) in the presence of bounded disturbances and NN functional reconstruction errors.