The presence of disturbances may bring adverse effects to the formation flight of multiple quadrotors. This paper proposes a robust disturbance observer-based feedback linearization that enhances the formation tracking control of quadrotors to achieve the desired formation shapes under the effect of disturbances. The method not only retains the simplicity of the control scheme using feedback linearized quadrotor model, but also has the capability to reject the disturbances. This is achieved by introducing a disturbance observer to estimate and attenuate the lumped disturbance that causes inexact inversion in the feedback linearization of the quadrotor. Then, a distributed formation tracking algorithm is adopted to ensure the quadrotors are able to form up and maintain the desired formation shape and heading via local communication between neighbours with respect to a leader that has nonzero control input. To evaluate the effectiveness of the proposed method, simulation experiments of multiple quadrotor formations using the proposed approach are conducted under several test cases. Results obtained demonstrate the superiority of the proposed control scheme for a more robust formation tracking as compared with the formation without the disturbance observer. Keywords Disturbance rejection, distributed control, feedback linearization, formation control, unmanned aerial vehicles

Cranes suffer from excessive swing due to the parameter uncertainties such as payload hoisting and payload mass which may affect the efficiency of the control performances. This paper presents modelling and optimal control of a gantry crane system subjected to parameter uncertainties. The Lagrange method is used for modelling the gantry crane which is derived from the Lagrange equation. An optimal controller is proposed in the system to obtain an efficient swing reduction and accurate trolley position. The proposed controller consists of two feedback control systems namely PID and PD controller which are utilized for tracking the accurate trolley position and reducing the payload swing angle, respectively. An intelligent optimization method is implemented in this study where the Particle Swarm Optimization (PSO) algorithm is used to find the optimal parameter gains for the controllers. For the robustness test, two cases are considered involving changes in payload mass and cable length. The superiority of the proposed method is confirmed by reduction of 17.7% in the maximum swing response over the comparative method.