Effective Formation Tracking of Quadrotors with Intelligent Disturbance Observer-Based Control

This study addresses the design and analysis of distributed formation tracking control with disturbance rejection capability for a group of quadrotors that are perturbed by time-varying external disturbances. The proposed approach consists of two control loops: inner-loop and outer-loop control. In the inner-loop control, feedback linearization (FL) of the quadrotor system in the presence of disturbance yields linear equations with unknown disturbance parts. The unknown disturbance parts are estimated in the outer-loop control by using a disturbance observer (DO). Since the DO produces an estimation error or called residual error when estimating time-varying disturbance, this study introduces a radial basis function neural network (RBFNN) with adaptive weight to effectively approximate and eliminate the residual error. Then, to complete the formation mission, a consensus-based algorithm is implemented in the outer-loop control to enable the distributed formation tracking. The capabilities of formation tracking and online approximating are proved via the Lyapunov approach. Simulation of the quadrotor formation in the presence of time-varying wind disturbance was conducted to evaluate the effectiveness of the approach where the results obtained illustrate the robustness of the quadrotor formation towards the disturbances.