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

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.

Tower crane is one of the flexible maneuvering systems that has been applied pervasively as a powerful big-scale construction machine. The under-actuated tower crane system has nonlinearity behavior with a coupling between translational and slew motions which increases the crane control challenge. In practical applications, most of the tower cranes are operated by a human operator which lead to unsatisfactory control tasks. Motivated to overcome the issues, this paper proposes a fuzzy logic controller based on single input rule modules dynamically connected fuzzy inference system for slew/translational positioning and swing suppressions of a 3 degree-of-freedom tower crane system. The proposed method can reduce the number of rules significantly, resulting in a simpler controller design. The proposed method achieves higher suppressions of at least 56% and 81% in the overall in-plane and out-plane swing responses, respectively as compared to PSO based PID+PD control.

This paper proposes using an image processing technique to detect the oscillation deflection of a quadrotor system carrying a suspended payload. A wireless smart camera is attached underneath the quadrotor to capture the payload oscillation in transit from one place to another. The image processing technique was used to measure the oscillation and response based on the output signal captured by the smart camera. A Parrot BEPOP quadrotor drone was used for the experiment and the image processing technique was realised using MATLAB software. Experiments were carried out to detect and analyse the payload oscillation response under different payload masses. The experiment results demonstrate the effectiveness of the image processing technique in detecting the payload oscillation under different payload masses.