In this paper, an anti-disturbance control for UAVs formations in a three-dimensional environment using relative information is proposed. To accurately describe the UAV cluster motion error in a three-dimensional environment, an error estimation model based on the UAV model is established to realize the UAV state estimation based on relative information. The control law to achieve the stability of the UAV formation under bounded perturbation is also derived using the backstepping control strategy. Next, to realize that the UAV formation can achieve optimal control under performance constraints, an algorithm based on model predictive control is proposed, and an ant colony algorithm is used to accelerate the solution process. Then, the stability of the UAV formation under the action of this algorithm is investigated. Finally, the superiority and effectiveness of the algorithm compared with the traditional backstepping control method are verified through comparative simulations.

The problem of vision-based fixed-wing UAV formation control under communication limitations and the presence of measurement errors was investigated. In the first part of this paper, the single UAV motion model and the process of estimating the neighboring UAV states using the Extended Kalman Filter are introduced. The second part describes how we designed a sliding mode controller considering the sensor measurement errors and discusses the sufficient conditions for the stability and formation system in the presence of state transfer time delays in the formation. The main motivation of this paper was to develop a hierarchical, globally stable sliding mode controller that could enable the considered vision-based multiple fixed-wing UAVs to achieve and maintain formation in the presence of measurement errors. To this end, the selected problem was first transformed into a state-tracking problem for UAVs in the neighborhood, and then the stability criterion was established using the Lyapunov stability theory. Finally, the effectiveness of the proposed control method was illustrated using three numerical arithmetic examples.

A distributed model predictive control method is used to transform the formation and maintenance problem of fixed-wing UAV formation during flight into an online rolling optimization problem to solve in this paper. Firstly, the state estimation model of the neighborhood UAV is established according to the relative information of the UAV. Secondly, the error state model in the three-dimensional inertial coordinate frame of the UAV is established without considering the time delay, sensor error, and external interference. Thirdly, a cost function is designed by introducing the error state of the UAV in the neighborhood. Finally, four UAVs are used to verify that under the action of the controller, the UAVs can quickly form and maintain the desired formation while tracking the reference line.