This paper discusses the design of a consensus tracking algorithm with the practical implementation of solving distributed altitude and attitude tracking of multiple quadrotors. In contrast to most of the existing works that consider a leader agent that is non-cooperative, this paper proposes an algorithm that allows the leader agent to receive feedback from a subset of the followers. Firstly, the feedback linearization technique is utilized on the nonlinear quadrotor dynamics which yield a simple linear system. Then, the proposed consensus tracking algorithm is designed and employed to the resulting linear system to achieve consensus tracking on altitude and attitudes via local interaction between neighbours. Results obtained show that the proposed consensus tracking for a group of quadrotors can improve connectivity preservation as compared to the non-cooperative leader in the presence of an obstacle. Based on the formulation of the controller, the methodology can be easily adapted to various systems such as attitude synchronization of multiple satellite systems.

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.

The presence of disturbances during flight may destabilize the quadrotor control and could compromise the designated mission. This paper proposes an improved quadrotor flight control in the presence of wind disturbances where the performance will be more robust in many flight conditions. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to improve the disturbance approximation and compensation. The AI technique via radial basis function neural network (RBFNN) is implemented to compensate the bounded estimation error produced by the disturbance observer. The weights of the neural network are tuned online with no prior training required. Simulation results demonstrate the effectiveness and feasibility of the proposed technique. Keywords: Disturbance observer, feedback linearization, neural network, quadcopter

The presence of disturbances can cause instability to the quadrotor flight and can be dangerous especially when operating near obstacles or other aerial vehicles. In this paper, a hybrid controller called state feedback with intelligent disturbance observer-based control (SF-iDOBC) is developed for trajectory tracking of quadrotor in the presence of time-varying disturbances, e.g. wind. This is achieved by integrating artificial intelligence (AI) technique with disturbance observer-based feedback linearization to achieve a better disturbance rejection capability. Here, the observer estimates the disturbances acting on the quadrotor, while AI technique using the radial basis func-tion neural network (RBFNN) compensates the disturbance estimation error. To improve the error compensation of RBFNN, the k-means clustering method is used to find the optimal centers of the Gaussian activation function. In addition, the weights of the RBFNN are tuned online using the derived adaptation law based on the Lyapunov method, which eliminates the offline training. In the simulation experiment conducted, a total of four input nodes and five hidden neurons are used to compensate for the error. The results obtained demonstrate the effectiveness and merits of the theoretical development.