Abstract
The purpose of this study is to offer an adaptive hybrid controller for the formation control of multiple unmanned aerial vehicles (UAVs) leader-follower configurations with communication delay. Although numerous studies about the control of the formation exist, very few incorporate the delay in their model and are adaptive as well. The motivation behind this article is to bridge that gap. The strategy consists of an adaptive fuzzy logic controller and a Proportional, Integral, and Derivative (PID) controller where the logic controller fines/tunes the PID controller gains. The controller also consists of an integrator that raises the order of the system which helps reduce the noise and steady-state errors. The simulations confirm that the proposed technique is robust and satisfies mission requirements. Moreover, the flying formations of the swarm were created by a B-spline curve based on a simple waypoint. The main contribution of this study is to present a model where the formation remains stable during the whole flight, errors are within the optimal range, and the time delays are manageable.
Highlights
Control of multi-UAV formation has become one of the challenging research areas among multiple UAV research issues. is surge in research interest in formation control is because of the various improvements of such multi-UAV formations over a single UAV, including superior adaptability, flexibility to unfamiliar surroundings, and robustness
Since the actual UAV formation system is often susceptible to interference factors such as aerodynamic interference, the traditional PID controller parameters will not change after setting out, and it is difficult to adapt to the high dynamic changes of the internal parameters of the formation system [38, 39]. e designed controller utilizes the adaptive property, which can adapt well to the changes of the internal dynamic parameters of the system and is suitable for handling complex nonlinear problems such as UAV formations [40]. e design of the controller with adaptive fuzzy PID control law can improve the control of the formation system effect
E inputs of fuzzy logic controllers are positioned and orientation errors along with their derivatives. It is in the range between − 1 and 1 used for the input error signals and their linguistic levels are defined as Positive High (PH), Positive Low (PL), Zero (ZR), Negative Low (NL), and Negative High (NH)
Summary
Control of multi-UAV formation has become one of the challenging research areas among multiple UAV research issues. is surge in research interest in formation control is because of the various improvements of such multi-UAV formations over a single UAV, including superior adaptability, flexibility to unfamiliar surroundings, and robustness. En, it uses the Lyapunov function to reduce it to an optimal level In another recent study [25], the authors control the formation of multiple UAVs using a distributed backstepping control technique. One study [27] designs a predictive model using the eventtriggered method for the control of multiple UAVs. In the proposed model, UAVs can only share information with their neighbors and the event trigger phenomenon helps lessen the computational stress on the algorithm. UAVs can only share information with their neighbors and the event trigger phenomenon helps lessen the computational stress on the algorithm Another hybrid strategy in [28] is to control the formation of a swarm of multiple UAVs by improving the fitness function.
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