Neural Network-Based Adaptive Fractional-Order Backstepping Control of Uncertain Quadrotors with Unknown Input Delays

Adaptive control is essential and effective for reliable quadrotor operations in the presence of uncertain modeling parameters and unknown time-delayed inputs. This paper presents an original radial basis function neural network-based adaptive fractional-order backstepping controller (RBF-ADFOBC). The nonlinearity of the time-delayed inputs is eliminated by introducing an augmented state variable via Pade's approximation method. For each subsystem in the quadrotor dynamics, a companioned second-order compensation system is developed. The candidate Lyapunov functions are then properly designed by incorporating the control errors, parameter uncertainties and estimation errors of the neural networks' weight vectors. It is shown that the semi-globally uniformly ultimately boundedness of all the state variables and the estimation error of uncertain parameters can be guaranteed. In addition, the trajectory-tracking error of the state variables can be driven to an adjustable small neighborhood of origin by properly setting the selectable parameters. Numerical simulations reveal that the tracking performance of the proposed controller can be improved continuously as the fractional order increases to a specific positive value, and the controller with a negative order may demonstrate higher robustness to the modeling uncertainties. Favorably, the comparison to the other two previous controllers further reveals the superior tracking accuracy and robustness of the proposed RBF-ADFOBC controller.