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.

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.

This brief proposed a cluster space control method to help the operator of manned aerial vehicles (MAVs) to coordinate with unmanned aerial vehicles (UAVs) more efficiently. The operator of the MAV could change the states of the whole heterogeneous team only by issuing a single command. To verify its application performance, the method is employed to conduct search tasks under a complex environment with static and dynamic unknown targets. The receding horizon control (RHC) and backstepping control methods are utilized to complete the search task. Simulation results show the feasibility and efficiency of the proposed method and give a strong foundation for man-machine coordination in related fields.

This article proposes an integrated framework for unmanned aerial vehicles (UAVs) cooperative control, combining advanced distributed control strategies with human–swarm interaction mechanisms to address obstacle avoidance scenarios. First, distributed controllers are designed to explicitly account for observer errors. Specifically, first-order and high-order control barrier functions (CBFs), integrated with the bounded-error observer, are proposed and theoretically validated. These CBFs impose constraints on the control inputs to guarantee system safety during the entire operation. Second, a three-tier human–UAV swarm interaction architecture is introduced, enabling comprehensive human intervention across different operational levels. To verify the effectiveness and practicality of the proposed method, simulation experiments are conducted in a target tracking and rescue scenario. The integrated observer–controller design demonstrates superior performance over conventional approaches, exhibiting enhanced obstacle avoidance capabilities and robust disturbance rejection. The three-tier framework can effectively coordinate human–UAV swarm interaction and improve the efficiency of the swarm mission.

Using the heterogeneous manned/unmanned aerial team in a dynamic environment offers a lot of benefits, but one needs an efficient system to coordinate their behavior. This article provides a three-dimension cluster space formation control method to allow one operator on the manned aircraft to supervise and control multiple unmanned systems simultaneously. In this method, the UAV swarm is regarded as an entity with specified cluster space states. Through this method, the cluster space commands issued by the operator could be converted to the UAV pose states. Via the combination of four designed cluster space commands, the heterogeneous team could complete the complex formation transformation in three-dimension space. To execute the commands from operators under the input constraint, the unmanned system utilizes the damping discrete time optimal control law to converge to the desired pose states. To validate the effectiveness of the proposed cluster space formation control method, experiments are conducted on both numerical simulations and platforms.

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.