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This brief investigates the target tracking control problem for a group of unmanned aerial vehicles (UAVs). Firstly, a neighbor selection mechanism is introduced to design the target state observer to guarantee the fast accessibility of target information to the uninformed UAVs. Then, a distributed tracking control protocol based on the estimated target information is proposed for dynamic target tracking. Meanwhile, proofs about the informed-to-uninformed reachability and the convergence of the group are provided. Finally, comparative simulations are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This brief investigates the multi-cluster consensus for heterogenous manned/unmanned aerial system with random link failure via pinning control method. Each cluster is composed of first-order and second-order dynamic models. A distributed control protocol is proposed to construct the weakly connected topology with restricted communication load in large-scale heterogenous aerial team. Based on the graph theory, matrix analysis and Lyapunov theorem, sufficient consensus conditions are derived. Numerical examples validate the theoretic results. [ABSTRACT FROM AUTHOR]

A distributed cooperative control algorithm of multiple unmanned aerial vehicles (UAVs) with directed topology is investigated in this brief. To overcome the actuator faults and external disturbance, incremental sliding mode formation controller is developed to realize the distributed coordination of the UAVs system, where an adaptive mechanism is utilized to deal with the chattering problem. Moreover, to reduce the adverse influence of input saturation, an auxiliary system is introduced to regulate the control signals. It is shown by Lyapunov method that the stability of the proposed algorithm can be guaranteed. A numerical example is given to illustrate that all follower UAVs can track the leader UAV. [ABSTRACT FROM AUTHOR]

Automatic Carrier Landing System With Fixed Time Control [ABSTRACT FROM AUTHOR]

Inspired by the background adaptive mechanism of eagle vision in different hunting environment, a biological eagle-eye vision adaptation mechanism algorithm is proposed for unmanned aerial vehicle (UAV) to detect the long-distance maritime small target in complex and changeable sea environment. First, the various environment adjustment abilities are summarized according to the physiological structures and characteristics of eagle vision. Second, the mathematical models of glaring adaptation, dim adaptation, color adaptation, and the background adaptation are established based on the background adaptation mechanisms of eagle vision. Last but not least, our proposed biological eagle-eye vision adaptation method and other five comparative experiments are implemented in different scenes, such as dazzling, cloudy, dusk, etc. The results of various evaluation indices show that the information of maritime small target is retained satisfactorily and the background of sea or sky is restrained effectively by the proposed algorithm. The maritime small target detection algorithm can be used for the vision system of UAV operating on the sea. It provides a feasible solution for UAV's remote vision autonomous navigation in changeable sea environment. [ABSTRACT FROM AUTHOR]

This article proposes a novel homing behavior model of pigeons and the simulation results are presented. A homing pigeon-inspired autonomous navigation system for an unmanned aerial vehicle (UAV) is further achieved. Experimental results on the designed simulation platform show that UAV can finish an autonomous return voyage similar to the homing pigeon. [ABSTRACT FROM AUTHOR]

The probe-and-drogue autonomous aerial refueling (AAR) docking suffers the multiwind disturbances leading to the low accuracy of receiver position control and drogue relative position estimation. This article proposes a bionic visual navigation control system in a hardware-in-loop simulation environment and further develops an AAR outfield experiment platform for promoting the success of probe-and-drogue AAR docking. The drogue region and markers are detected by the biological method imitating the eagle-eye color vision mechanism. The different visual navigation methods based on the ellipse fitting, marker matching, and marker prediction are, respectively, applied to the three possible situations in AAR docking for precise pose estimation. Moreover, a relative position control scheme of the receiver, which is constructed by back-stepping design technique and gain-adaptive equivalent sliding mode control, is proposed to compensate multi-wind disturbances and model uncertainties. Fuzzy logic position strategy is designed to compensate swing of the drogue caused by the multiwind disturbances. The AAR outfield experiment platform contains two unmanned aerial vehicles to verify a part of the proposed navigation control method. Extensive experimental results are presented to demonstrate the effectiveness of the bionic visual navigation control system for the high accuracy of the visual navigation and antidisturbance position control. [ABSTRACT FROM AUTHOR]

For multiple unmanned aerial vehicles (UAVs) performing aerial search-attack tasks, there is a tradeoff between maximizing total benefit and minimizing consumption under the validity of constraints. This article proposes a dynamic discrete pigeon-inspired optimization algorithm to handle cooperative search-attack mission planning for UAVs, which integrates the centralized task assignment and distributed path generation aspects of the problem. Besides, a solution acceptance strategy is proposed to avoid frequent task switching. To design a reasonable objective function, the probability map is constructed and updated by Bayes formula to guide the following search motion, and a response threshold sigmoid model is adopted for target allocation during executing attack. Moreover, the flyable trajectories are generated by B-spline curves based on the simplified waypoints. Finally, numerical experiments prove that the proposed methods can provide feasible solutions for multiple UAVs considering different scenarios, such as the absence or presence of threats and insufficient resources. The results also show that the solution acceptance strategy is effective to improve performance. Moreover, the extensible mission planning system also integrates with an interactive 3D visualization simulation module, where the multi-UAV coordinated flight processes are demonstrated dynamically. [ABSTRACT FROM AUTHOR]

Cooperative target enclosing problem with multiple unmanned microaerial vehicles is addressed. A local controller based on a binary-tree (BT) communication topology is presented for the target enclosing formation. A sufficient condition is derived for the formation stability by a recursive Lyapunov method. In this article, a switching rule of the BT topology is developed to cope with the emergent failures of target sensing and the formation scalabilities. Besides, in order to accommodate the enclosure in complicated environments, a multitask framework is constructed which is compatible with the local control protocols. Comparative experiments on both simulations and platforms demonstrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This article presents a limit-cycle-based mutant multiobjective pigeon-inspired optimization (PIO). In this algorithm, the limit-cycle-based mechanism is devised to consider the factors that affect the flight of pigeons to simplify the multiobjective PIO algorithm. The mutant mechanism is incorporated to strengthen the exploration capability in the evolutionary process. Additionally, the application of the dual repository makes the nondominated solutions stored and selected to guide the flight of pigeons. Attributed to the limit-cycle-based mutant mechanisms, this algorithm not only obtains the faster convergence speed and higher accuracy but also improves its population diversity. To confirm the universal application of this algorithm, theoretical analysis of the convergence is discussed in this article. Finally, comparative experiments of our proposed algorithm and other five multiobjective methods are conducted to verify the accuracy, efficiency, and convergence stability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article proposes an enhanced active disturbance rejection control (ADRC) method for the attitude deformation system of a self-developed mobile robot in conjunction with evolutionary game theory-based pigeon-inspired optimization (EGPIO). To achieve precise control of the object, EGPIO is employed as an automatic tuning mechanism for ADRC, and simultaneously improves the rapidity and robustness of the controller. In this novel algorithm, individuals in pigeon-inspired optimization (PIO) adjust their strategies dynamically as the evolutionary game proceeds so as to promote the adaptability of the original PIO. Thus, the optimal solutions calculated by EGPIO will be adopted as the main parameters of ADRC to control the mobile robot. The effectiveness of the novel controller is validated by a series of simulations, including cases with normal conditions and situations with disturbances. Experimental results indicate that the scheme we designed possesses better control performances and shows attractive advantages for efficiency and fault tolerance compared with conventional ADRC. [ABSTRACT FROM AUTHOR]

This paper presents a predator-prey pigeon-inspired optimization (PPPIO) algorithm for an automatic landing system of a fixed-wing unmanned aerial vehicle (UAV) in the longitudinal plane. A pitch command autopilot and two types of approach power compensators are presented and evaluated in automatic landing. The design of a glide slope command system and an approach power compensator are converted to a finite-dimensional optimization problem. The longitudinal flight controller parameters are optimized by the proposed PPPIO algorithm. The differences between the two proposed approach power compensators are also analyzed. The proposed optimization process can guarantee the control gains vector converge to an approximate optimal solution, and it is computationally much more efficient. Simulation results are presented to demonstrate that this approach helps solve control system optimization problems for different criteria and attains a rather accurate result. As verified by these simulation results, the proposed automatic landing system can help improve UAV's performance during the landing phase. [ABSTRACT FROM AUTHOR]

In this paper, the systematic design and implementation of a biological eagle-eye-based visual platform is presented for target detection. Inspired by the structure and properties of the eagle eye, the hardware configuration of the visual platform is presented, and software programs including image stitching and target detection are developed. The saliency analysis method is adopted to estimate the position of the potential target. The optical triangulation is utilized to obtain the relative azimuth information of the estimated target. Comparative experimental results show that the overall platform is efficient at target detection. [ABSTRACT FROM AUTHOR]

Three-dimension path planning of uninhabited combat aerial vehicle (UCAV) is a complicated optimal problem, which mainly focused on optimizing the flight route considering the different types of constrains under complex combating environment. A novel predator-prey pigeon-inspired optimization (PPPIO) is proposed to solve the UCAV three-dimension path planning problem in dynamic environment. Pigeon-inspired optimization (PIO) is a new bio-inspired optimization algorithm. In this algorithm, map and compass operator model and landmark operator model are used to search the best result of a function. The prey-predator concept is adopted to improve global best properties and enhance the convergence speed. The characteristics of the optimal path are presented in the form of a cost function. The comparative simulation results show that our proposed PPPIO algorithm is more efficient than the basic PIO, particle swarm optimization (PSO), and different evolution (DE) in solving UCAV three-dimensional path planning problems. [ABSTRACT FROM PUBLISHER]