Trajectory tracking control of unmanned aerial vehicle with disturbance observer and robust adaptive neural dynamic surface

This paper investigates the trajectory tracking control problem for a class of uncertain strict feedback nonlinear systems subject to unknown dynamics and time varying external disturbances, with a specific application to unmanned aerial vehicle (UAV) longitudinal motion. A novel robust adaptive neural dynamic surface control (DSC) scheme integrated with nonlinear disturbance observers (DOB) is proposed. First, nonlinear disturbance observers are systematically constructed for each subsystem to provide real-time estimates of unknown bounded disturbances, and these estimates are explicitly incorporated into both virtual and actual control laws for active compensation, significantly enhancing disturbance rejection capability. Second, radial basis function (RBF) neural networks are employed to approximate the unknown continuous functions arising from system dynamics, and a parameter aggregation strategy is adopted to reduce the number of online adaptation parameters, thereby simplifying the implementation. Third, the dynamic surface control technique is utilized to overcome the “explosion of complexity” inherent in conventional backstepping designs, eliminating the need for analytical differentiation of virtual control laws. Numerical simulations demonstrate superior tracking accuracy and disturbance rejection capability compared to conventional adaptive neural DSC without disturbance observers, validating the effectiveness and robustness of the proposed scheme.