Convergence Analysis on Multi-AUV Systems With Leader-Follower Architecture

Xing, Wen; Zhao, Yuxin; Karimi, Hamid Reza

doi:10.1109/access.2017.2651048

Cited by 58 publications

(18 citation statements)

References 42 publications

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“…The scenario of the online task assignment is set as: there are two UAVs performing nine tasks of three targets in a 2m×2m space The initial offline task scheme using the two-part individual representation is [1,2,3,4,5,6,7,8,9,6,3]. The decision variable T' and the finish time Ta are respectively 5s and 9.9513s.…”

Section: Online Hierarchical Planning Algorithm For the Uav Task Amentioning

confidence: 99%

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Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Chen

Yang

2018

IEEE Trans. Aerosp. Electron. Syst.

130

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This paper presents a systematical framework to solve the multiple unmanned aerial vehicles (multi-UAV) cooperative task assignment problem. Based on a combinatorial optimization model, it is solved by a digraph-based method and a novel meta-heuristic optimization method, named modified two-part wolf pack search (MTWPS) algorithm. When the number of UAVs/targets is large, in order to reduce the simulation time, we also present a new solution framework based on an easy-computing objective function. Additionally, the parameter and time-sensitive uncertainty are considered in the extended task assignment problem. For the problem with parameter uncertainty, it is formulated by a robust optimization method and solved by a novel combined algorithm, including the classical interior point method and our MTWPS algorithm. For the problem with time-sensitive uncertainty, it is solved by a practical online hierarchical planning algorithm. Finally, numerical simulations and physical experiments demonstrate that the proposed methods can provide a flyable solution for the UAVs and achieve outstanding performance in comparison with other algorithms. Index Terms-Multiple unmanned aerial vehicles (multi-UAV) cooperative task assignments problem, modified two-part wolf pack search (MTWPS) algorithm, robust optimization method, online hierarchical planning algorithm Yongbo Chen received his B.S. degree in Beijing Institute of Technology in 2012. He is currently working toward a dual doctoral degree at Beijing

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Section: Online Hierarchical Planning Algorithm For the Uav Task Amentioning

confidence: 99%

“…Different from the formation control in [1], the multi-UAV task assignment is suitable for loose formulation structure. In general, it is a sophisticated combinatorial optimization problem with complex constraints, in consideration of task precedence and coordination, timing constraints, and flyable trajectories.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Chen

Yang

2018

IEEE Trans. Aerosp. Electron. Syst.

130

View full text Add to dashboard Cite

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“…Other noteworthy extensions which appeared in the formation control are self tuning control [5], adaptive control [6]- [9], gain scheduling control [10], linear quadratic Gaussian control [11], nonlinear tracking control [12]. In order to solve the consensus tracking control problem, a great deal of algorithms were developed, i.e., linear conventional PD/PID control [13], the method of leader-follower [14], [15], backstepping control [16], integrator backstepping method [17], output-feedback tracking controllers [18], dynamic surface control technique [19], genetic algorithm based control [20], sliding mode control [21], [22], and approach of graph theory analysis [23]. From the existing literature review, it has been investigated that graph theory analysis has great advantages for dealing with distributed control problem because it can simplify communication topology and optimize information model of multi-AUV system.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Finite-Time Consensus Tracking Control for Multiple Autonomous Underwater Vehicles

Cui

Zhao

et al. 2019

IEEE Access

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Considering the problem of consensus tracking control for multiple autonomous underwater vehicle (AUV) system, a neural network-based finite-time nonsingular fast terminal sliding mode control method is proposed. First, in order to elaborate on the communication relationship, the algebraic graph theory is combined with a leader-follower architecture. Next, the modified nonsingular fast terminal sliding mode is adopted to improve the fast response characteristic of the system, and distributed control laws are constructed based on the force analysis of each AUV. Furthermore, neural networks technique is employed to approximate the uncertain dynamics and forces caused by the harsh environment of the ocean. Finally, it is proved that the tracking errors can converge to a small neighborhood of the origin by using the proposed algorithm. The effectiveness and robustness of the proposed algorithm are illustrated by a simulation example. INDEX TERMS Adaptive neural control, multiple AUV system, nonsingular terminal sliding mode, finite time, graph theory.

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“…Wen proposed an Unscented Particle Filter (UPF) algorithm for cooperative navigation with two leaders, which can effectively reduce the impact of sensor noise [9]. Gao established a cooperative navigation algorithm for two leader AUVs, which solved the weakness of the observability of the system and reduced the requirement for the maneuverability of the leader AUVs [10].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Navigation Based on Cross Entropy: Dual Leaders

Zhang

Liu

et al. 2019

IEEE Access

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Cooperative navigation aims at improving positioning accuracy of Autonomous Underwater Vehicles (AUVs). In this paper, a dual leaders cooperative navigation method is proposed based on Cross Entropy (CE) algorithm. Since the trajectories of the slave AUVs are assumed to be predetermined, the Markov Decision Process (MDP) is also integrated in the proposed algorithm to generate optimal trajectories of master AUVs from the perspective of probability. Firstly, the navigation model and cost functions are established for the cooperative navigation system with multiple masters and slaves. Then, the CE algorithm is used to train the system with help of MDP to obtain the path of the master AUVs. In the simulation, the cooperative localization trajectories of the slave AUVs are obtained by Extended Kalman Filter (EKF) and are compared with other positioning methods. The results show that the trajectories of dual master AUVs obtained by the proposed algorithm can not only reduce the observation error of the slave AUVs in the system effectively, but also keep relative measurement distance between the master AUVs and the slave AUVs in a suitable range. INDEX TERMS Cooperative navigation, cross entropy, Markov decision process, path planning.

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Convergence Analysis on Multi-AUV Systems With Leader-Follower Architecture

Cited by 58 publications

References 42 publications

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Multi-UAV Task Assignment With Parameter and Time-Sensitive Uncertainties Using Modified Two-Part Wolf Pack Search Algorithm

Neural Network-Based Adaptive Finite-Time Consensus Tracking Control for Multiple Autonomous Underwater Vehicles

Cooperative Navigation Based on Cross Entropy: Dual Leaders

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