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Unmanned aerial vehicle (UAV) swarms offer unique advantages for area search and environmental monitoring applications. For practical deployments, determining the optimal number of UAVs required for a given task and defining key performance metrics for the platforms and payloads are crucial challenges. This study aims to address mission planning and performance optimization for cooperative UAV swarm search scenarios. A new clustering algorithm is proposed, integrating enhanced clustering techniques with ant colony optimization, particle swarm optimization, and crow search optimization. This jointly optimizes and validates the UAV numbers and coordinated trajectories. Sensitivity analysis and indicator optimization further examine specific scenarios to quantify platform and sensor factors influencing search efficiency. Lastly, sensitivity analysis and performance indicator optimization are conducted in specific scenarios. The modular algorithmic components and modeling techniques established in this work lay a foundation for continued research into real−world mission−based swarm optimization. [ABSTRACT FROM AUTHOR]

Summary: This article addresses the distributed adaptive synchronization tracking control problem for multiple flying‐wing UAVs with predefined accuracy and unknown input nonlinearities. Considering that the multiple flying‐wing unmanned aerial vehicles (UAVs) system is a large‐scale nonlinear and strongly coupled system, the control schemes in existing literature which only consider longitudinal, lateral or attitude control may not be applicable in practical engineering. Thus, a full‐state flying‐wing UAV model containing both translational and rotational motions is established, whose dynamics are six‐degree‐of‐freedom with twelve‐state‐variables. Furthermore, to handle the problem that the prior knowledge of the actuator nonlinearities of multi‐UAV system are difficult to obtain, a more general input nonlinear model is established and the adaptive boundary estimation technique is developed. Finally, a performance‐oriented controller is proposed by incorporating a series of smooth functions into adaptive neural control design and Lyapunov analysis. Theoretical analysis and simulation results show that the presented control scheme can guarantee the satisfactory transient tracking error performance, and the tracking error converges to a user‐defined interval ultimately. [ABSTRACT FROM AUTHOR]

Cooperative group formation control of manned/unmanned aircraft vehicles (MAV/UAVs) using a hierarchical framework can be more efficient and flexible than centralized control strategies. In this paper, a two-level hierarchical-interaction-based cooperative control strategy is proposed for the MAV/UAVs group formation. At the upper level, combined with the nonlinear disturbance observer (NDO) and dynamic surface control (DSC) algorithm, a trajectory tracking problem with external disturbances for MAV is formulated. At the lower level, the leader-following formation controller is utilized to deal with the sub-formation keeping control problem for UAVs, based on the sliding mode disturbance observer and fast terminal sliding mode control law, and the robust performance and control accuracy are effectively improved. Moreover, the overall stability of the MAV/UAVs system is demonstrated using Lyapunov theory. The proposed approach is evaluated by simulation under the ground penetration combat mission for MAV/UAVs, and the performance is compared with that of other control strategies. [ABSTRACT FROM AUTHOR]

Compared with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this paper proposes an event-based fault-tolerant coordinated control (FTC) for multiple fixed-wing UAVs such that the consensus tracking of velocity and attitude is achieved in the presence of actuator faults, external disturbances and modeling uncertainties. More precisely, as opposed to static event-triggered communication mechanisms, a dynamic event-triggered communication mechanism (DECM) is devised to schedule the connected communications while avoiding the unnecessary information exchanges among UAVs, which reduces the communication burden and saves on the network resources. Meanwhile, the Zeno phenomenon is excluded in terms of guaranteeing that the period between two consecutive triggering communication is lower bounded by a positive constant. Moreover, the actuator fault, external disturbance as well as model uncertainty are treated as the lumped disturbances and estimated via the disturbance observer technique. By strict Lyapunov arguments, all closed-loop signals are proved to be uniformly ultimately bounded (UUB) and the tracking errors of velocity and attitude converge to a residual set around origin. Finally, simulation results are presented to illustrate the validity and superiority of proposed event-based control scheme. [ABSTRACT FROM AUTHOR]

This paper is devoted to the cooperative tracking control of multiple unmanned aerial vehicles with unknown faults. Considering the practical case that the unmanned aerial vehicles suffer from strong nonlinearities, external disturbances, actuator, and sensor faults, this brief investigates the distributed adaptive fault tolerant scheme for multi‐unmanned aerial vehicle system within model predictive control framework. Firstly, for preparation to the fault occurrence case, the baseline model predictive control scheme is designed under fault‐free case. Then considering the fault occurrence, a fault detection strategy is proposed by means of the moving horizon estimation and linearly parameterized approximation for actuator and sensor faults, respectively. Thereby with the characterized fault information, the fault tolerant model predictive control scheme is constructed by using an adaptive updating mechanism to compensate for actuator and sensor faults simultaneously. Finally, simulations well demonstrate the effectiveness of proposed control scheme. [ABSTRACT FROM AUTHOR]

In contrast with most existing results concerning unmanned aerial vehicles (UAVs) wherein two-degree or only attitude/longitudinal dynamics are considered, this article proposes an event-triggered cooperative synchronization fault-tolerant control (FTC) methodology for multiple fixed-wing UAVs whose dynamics are six-degree-of-freedom (DOF) with twelf-state-variables subject to actuator faults, modeling uncertainties, and external disturbances. More precisely, an event-triggering mechanism is devised to determine the time instants of updating control signals, which reduces the signal transmission burden, while saving on system resources. The Zeno phenomenon is excluded in the sense of guaranteeing that the time between two consecutive switchings is lower bounded by a positive constant. The actuator faults as well as the network induced errors are handled via the bound estimation approach and some well-defined smooth functions. By strict Lyapunov arguments, all closed-loop signals are proved to be semi-globally uniformly ultimately bounded (SGUUB) and the synchronization tracking errors of speed and attitude converge to a residual set around origin whose size can be made arbitrarily small through selecting appropriate design parameters. [ABSTRACT FROM AUTHOR]

Summary: This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the disturbance observer-based model predictive control approach to deal with the unmanned aerial vehicle formation flight with unknown disturbances. The distributed control problem for a class of multiple unmanned aerial vehicle systems with reference trajectory tracking and disturbance rejection is formulated. Firstly, a local distributed controller is designed by using the model predictive control method to achieve stable tracking, where the local optimization problem is solved by an adaptive differential evolution algorithm. Then, a feedforward compensation controller is introduced by using a disturbance observer to estimate and compensate disturbances, and improve the ability of anti-interference. Besides, the stability of the proposed composite controller is analyzed as well. Finally, the simulation examples are provided to illustrate the validity of proposed control structure. [ABSTRACT FROM AUTHOR]