# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
Adaptation elements were introduced into the control loop to mitigate the influence of the disturbances. The efficacy of the proposed control system was illustrated both numerically and experimentally by indoor and outdoor flights. [PERSON] and [PERSON] [23] examined the messaging process for coordinating decisions within a fully autonomous UAV group in the overall task of Plume Wrapping, which is used to determine the shape and size of airborne hazardous materials. This is a practical problem involving a real scenario. An algorithm was proposed to ensure the correctness of operation and resistance to variable delays when receiving messages. In [24], a fractional order and trajectory control system was proposed for several quadrotors in formation using Super Twisting Sliding Mode Control (STSMC) technology. The Lyapunov function method was employed to synthesize controllers able to compensate for the influence of parametric uncertainties and wind gusts and provide justify the stability of the control system. Three types of controllers were considered: a fixed-gain STSMC, an Adaptive Super Twisting Sliding Mode Control (ASTSMC), and a Fractional-order Adaptive Super Twisting Sliding Mode Control (FASTSMC). These were tested by UAV swarm simulation. From the authors' simulation results, the FASTSMC method demonstrated better robustness compared to the fixed-gain STSMC and integer-order ASTSMC. For an underactuated quadrotor, [PERSON] et al. [25] proposed an adaptive robust hierarchical control strategy. Based on the geometric approach, the orientation error in [25] was directly defined in the tangent space of the rotation group. To eliminate the influence of uncertainties, it was proposed to use a detailed dynamic model to represent the main aerodynamic and mechanical parameters of the quadrotor, and this was used to organize direct/indirect adaptive control. The presented simulation results showed the good performance of the system based on the proposed method in terms of tracking accuracy and robustness. In a recent paper by [PERSON] et al. [26], the formation control of multiple fixed-wing unmanned aircraft vehicles (UAVs) based on a virtual leader structure and subject to unknown uncertainties and disturbances was studied. A fully distributed model reference robust adaptive controller (DMRRAC) was proposed, with the aim of swiftly constructing the formation in complex situations. This approach was able to dramatically reduce reliance on UAV models and global info. Meanwhile, an adaptive projection formation operator and novel high-frequency robust term of the controller were used to guarantee that the formation system was uniformly asymptotically stable under the influences of uncertainties and disturbances, and could additionally enhance the transient performance and robustness. Validation and comparative simulations were implemented to demonstrate the proposed DMRRAC method. A platform based on ROS was used to verify the actual operability of the controller. The problem of controlling distributed formation vertical take-off and landing (VTOL) UAVs under the action of unknown perturbations was studied in [27]. Two algorithms for distributed formation control with a hierarchical structure were developed. Methods were proposed to guarantee limited thrust, work without information about acceleration, and compensation for the influence of external disturbances. In [28], a method was proposed to ensure the overall stability of a quadrotor-type UAV formation with significant variations in the parameters of individual UAVs. It was presumed that the desired trajectory is known only to a single UAV in the formation (\"the leading UAV\"). For the synthesis of local controllers, a signal-parametric adaptive control based on the passification method [29; 30] was employed. For illustration, the simulation results for altitude control of a group of 25 quadrotors were shown. This result was extended to the adaptive control of spatial motion for a group of quadrotors in [31].