Multiple Unmanned Underwater Vehicles Consensus Control with Unmeasurable Velocity Information and Environmental Disturbances Under Switching Directed Topologies

A consensus algorithm proposed in the paper is applied to tackle remarkable problems of unmeasurable velocities,the environmental disturbances, and the limited communication environment for the multiple unmanned underwater vehicles(multi-UUVs). Firstly, for a complex nonlinear and coupled model of the unmanned underwater vehicle(UUV), a technique of feedback linearization is developed to transform the nonlinear UUV model into a secondorder integral UUV model. Secondly, to address the problem of the unavailable velocity information and environmental disturbances for the multi-UUVs system, we design a distributed extended state observer(DESO) to estimate the unmeasurable velocities and environmental disturbances using the relative position information. Finally,we propose a protocol based on the estimation information from the DESO and demonstrate that the multi-UUVs system with the switching directed topologies under the protocol can reach consensus asymptotically. The theoretical result proposed in the literature is verified by one numerical example.

Leader-following coordination of multiple UUVs formation under two independent topologies and time-varying delays

The new method which uses the consensus algorithm to solve the coordinate control problems of multiple unmanned underwater vehicles （multi-UUVs） formation in the case of leader-following is adapted. As the communication between the UUVs is difficult and it is easy to be interfered under the water, time delay is assumed to be time-varying during the members communicate with each other. Meanwhile, the state feedback linearization method is used to transfer the nonlinear and coupling model of UUV into double-integrator dynamic. With this simplified double-integrator math model, the UUV formation coordinate control is regarded as consensus problem with time-varying communication delays. In addition, the position and velocity topologies are adapted to reduce the data volume in each data packet which is sent between members in formation. With two independent topologies designed, two cases of communication delay which are same and different are considered and the sufficient conditions are proposed and analyzed. The stability of the multi-UUVs formation is proven by using Lyapunov-Razumilkhin theorem. Finally, the simulation results are presented to confirm and illustrate the theoretical results.

Threat sequencing of multiple UCAVs with incomplete information based on game theory

The threat sequencing of multiple unmanned combat air vehicles(UCAVs) is a multi-attribute decision-making(MADM)problem. In the threat sequencing process of multiple UCAVs,due to the strong confrontation and high dynamics of the air combat environment, the weight coefficients of the threat indicators are usually time-varying. Moreover, the air combat data is difficult to be obtained accurately. In this study, a threat sequencing method of multiple UCAVs is proposed based on game theory by considering the incomplete information. Firstly, a zero-sum game model of decision maker( D) and nature(N)with fuzzy payoffs is established to obtain the uncertain parameters which are the weight coefficient parameters of the threat indicators and the interval parameters of the threat matrix. Then,the established zero-sum game with fuzzy payoffs is transformed into a zero-sum game with crisp payoffs(matrix game) to solve. Moreover, a decision rule is addressed for the threat sequencing problem of multiple UCAVs based on the obtained uncertain parameters. Finally, numerical simulation results are presented to show the effectiveness of the proposed approach.

A Modified Self-Adaptive Sparrow Search Algorithm for Robust Multi-UAV Path Planning

With the advancement of technology,the collaboration of multiple unmanned aerial vehicles(multi-UAVs)is a general trend,both in military and civilian domains.Path planning is a crucial step for multi-UAV mission execution,it is a nonlinear problem with constraints.Traditional optimization algorithms have difficulty in finding the optimal solution that minimizes the cost function under various constraints.At the same time,robustness should be taken into account to ensure the reliable and safe operation of the UAVs.In this paper,a self-adaptive sparrow search algorithm(SSA),denoted as DRSSA,is presented.During optimization,a dynamic population strategy is used to allocate the searching effort between exploration and exploitation;a t-distribution perturbation coefficient is proposed to adaptively adjust the exploration range;a random learning strategy is used to help the algorithm from falling into the vicinity of the origin and local optimums.The convergence of DRSSA is tested by 29 test functions from the Institute of Electrical and Electronics Engineers(IEEE)Congress on Evolutionary Computation(CEC)2017 benchmark suite.Furthermore,a stochastic optimization strategy is introduced to enhance safety in the path by accounting for potential perturbations.Two sets of simulation experiments on multi-UAV path planning in three-dimensional environments demonstrate that the algorithm exhibits strong optimization capabilities and robustness in dealing with uncertain situations.

Multi-UAV Collaborative Trajectory Planning for 3D Terrain Based on CS-GJO Algorithm

Existing solutions for collaborative trajectory planning using multiple UAVs suffer from issues such as low accuracy,instability,and slow convergence.To address the aforementioned issues,this paper introduces a new method for multiple unmanned aerial vehicle(UAV)3D terrain cooperative trajectory planning based on the cuck0o search golden jackal optimization(CS-GJO)algorithm.A model for single UAV trajectory planning and a model for multi-UAV collaborative trajectory planning have been developed,and the problem of solving the models is restructured into an optimization problem.Building upon the original golden jackal optimization,the use of tent chaotic mapping aids in the generation of the golden jackal's inital population,thereby promoting population diversity.Subsequently,the position update strategy of the cuckoo search algorithm is combined for purpose of update the position information of individual golden jackals,effectively preventing the algorithm from getting stuck in local minima.Finally,the corresponding nonlinear control parameter were developed.The new parameters expedite the decrease in the convergence factor during the pre-exploration stage,resulting in an improved overall search speed of the algorithm.Moreover,they attenuate the decrease in the convergence factor during the post-exploration stage,thereby enhancing the algorithm's global search.The experimental results demonstrate that the CS-GJO algorithm efficiently and accurately accomplishes multi-UAV cooperative trajectory planning in a 3D environment.Compared with other comparative algorithms,the CS-GJO algorithm also has better stability,higher optimization accuracy,and faster convergence speed.

Consensus-based three-dimensional multi-UAV formation control strategy with high precision

We propose a formation control strategy for multiple unmanned aerial vehicles （multi-UAV） based on second-order consensus, by introducing position and velocity coordination variables through neighbor-to-neighbor interaction to generate steering commands. A cooperative guidance algorithm and a cooperative control algorithm are proposed together to maintain a specified geometric configuration, managing the position and attitude respectively. With the whole system composed of the six-degree-of-freedom UAV model, tile cooperative guidance algorithm, and the cooperative control algorithm, the formation control strategy is a closed-loop one and with full states. The cooperative guidance law is a second-order consensus algorithm, providing the desired acceleration, pitch rate, and heading rate. Longitudinal and lateral motions are jointly considered, and the cooperative control law is designed by deducing state equations. Closed-loop stability of the formation is analyzed, and a necessary and sufficient condition is provided. Measurement errors in position data are suppressed by synchronization technology to improve the control precision. In the simulation, three-dimensional formation flight demonstrates the feasibility and effectiveness of the formation control strategy.

Periodic event-triggered formation control for multi-UAV systems with collision avoidance

In this paper,periodic event-triggered formation control problems with collision avoidance are studied for leader–follower multiple Unmanned Aerial Vehicles(UAVs).Firstly,based on the Artificial Potential Field(APF)method,a novel sliding manifold is proposed for controller design,which can solve the problem of collision avoidance.Then,the event-triggered strategy is applied to the distributed formation control of multi-UAV systems,where the evaluation of the event condition is continuous.In addition,the exclusion of Zeno behavior can be guaranteed by the inter-event time between two successive trigger events have a positive lower bound.Next,a periodic event-triggered mechanism is developed for formation control based on the continuous eventtriggered mechanism.The periodic trigger mechanism does not need additional hardware circuits and sophisticated sensors,which can reduce the control cost.The stability of the control system is proved by the Lyapunov function method.Finally,some numerical simulations are presented to illustrate the effectiveness of the proposed control protocol.

Energy-efficient trajectory planning for amulti-UAV-assisted mobile edge computing system

We study a mobile edge computing system assisted by multiple unmanned aerial vehicles(UAVs),where the UAVs act as edge servers to provide computing services for Internet of Things devices.Our goal is to minimize the energy consumption of this system by planning the trajectories of UAVs.This problem is difficult to address because when planning the trajectories,we need to consider not only the order of stop points(SPs),but also their deployment(including the number and locations)and the association between UAVs and SPs.To tackle this problem,we present an energy-efficient trajectory planning algorithm(TPA)which comprises three phases.In the first phase,a differential evolution algorithm with a variable population size is adopted to update the number and locations of SPs at the same time.In the second phase,the k-means clustering algorithm is employed to group the given SPs into a set of clusters,where the number of clusters is equal to th at of UAVs and each cluster contains all SPs visited by the same UAV.In the third phase,to quickly generate the trajectories of UAVs,we propose a low-complexity greedy method to construct the order of SPs in each cluster.Compared with other algorithms,the effectiveness of TPA is verified on a set of instances at different scales.

Multi-UAV Cooperative Trajectory Planning Based on Many-Objective Evolutionary Algorithm

The trajectory planning of multiple unmanned aerial vehicles(UAVs)is the core of efficient UAV mission execution.Existing studies have mainly transformed this problem into a single-objective optimization problem using a single metric to evaluate multi-UAV trajectory planning methods.However,multi-UAV trajectory planning evolves into a many-objective optimization problem due to the complexity of the demand and the environment.Therefore,a multi-UAV cooperative trajectory planning model based on many-objective optimization is proposed to optimize trajectory distance,trajectory time,trajectory threat,and trajectory coordination distance costs of UAVs.The NSGA-III algorithm,which overcomes the problems of traditional trajectory planning,is used to solve the model.This paper also designs a segmented crossover strategy and introduces dynamic crossover probability in the crossover operator to improve the solving efficiency of the model and accelerate the convergence speed of the algorithm.Experimental results prove the effectiveness of the multi-UAV cooperative trajectory planning algorithm,thereby addressing different actual needs.

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requirements are encountered.This paper establishes a decision-making framework for multiple unmanned aerial vehicles(multi-UAV)based on the well-known pigeon-inspired optimization(PIO)algorithm.By addressing the problem from a hierarchical structural perspective,the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements,utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV.In the second stage,building upon the decisions made in the preceding stage,each UAV is abstracted as an agent maximizing its own task execution benefits.An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed,enabling the acquisition of the optimal task execution sequence.Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology.Moreover,dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.