Formation controller of multiple unmanned underwater vehicle based passivity design

The cooperative localization (CL) problem for multiple unmanned underwater vehicles (MUUVs) with communication delay is considered. In the master-slaver cooperation structure, the master UUV's relative localization information (RLI) is transmitted to master UUVs via acoustic communication. Due to the terrible underwater environment, measurements are subject to random delay. Moving horizon estimation (MHE) method with DEKF based arrive cost update law is presented in this paper to obtain accurate and reliable estimation of present state. At the end of this paper, simulation results are shown to prove the superiority and practicality of the proposed MHE algorithm.

This paper presents a new distributed decentralized architecture for the organization, command, control and communication (C/sup 3/) of multiple agent system which has to execute a mission, cooperatively and autonomously. This architecture can be applied to multiple unmanned underwater vehicles (UUVs), to unmanned air vehicles (UAVs), as well as to unmanned ground vehicles (UGVs), unmanned space vehicles (USVs) or to any other types of autonomous cooperative multiple agent systems. The rationale for autonomous cooperative operation of a multiple agent system, stems from the need to execute critical missions under time, space resources and availability constraints, which are beyond the capability of a single agent to perform successfully. If the system agents are distributed geographically, cooperative operation is a reasonable approach. It supports share of information, share of resources, efficient resource allocation, context and situation driven responsiveness, robustness and flexibility under changing conditions, as well as redundancy. A key feature of the proposed architecture is that all the agents are identical in their sensing, information processing, decision making, communication and mission related capabilities. Thus, a failed agent can be functionally replaced by a peer agent. The architecture is both distributed and decentralized. It is implemented via four major building blocks, which are embedded within each agent.

Multiple unmanned underwater vehicles (multi-UUVs) in the swarm can effectively carry out a task when they are successfully exchanging information among themselves in the ocean, so the paper mainly investigates the limited communication consensus control problem for the multi-UUVs swarm systems. The paper proposes a hybrid structure of the multi-UUVVs swarm. The multi-UUVs swarm system will be divided into many sub-groups in where it will be composed of one leader and many followings of UUVs, meanwhile, all leaders in the sub-groups will constitute a leaders-group. Two suitable Lyapunov-Krasovskii functional candidates will be given to obtain sufficient consensus conditions and multiple randomly varying leader-following consensus stability criterion for Markovian switching swarm system. Then the swarm system will be proved to be stochastically stable. Finally, a numerical example is shown to demonstrate the effectiveness of the discussed methods.

This study examined the interactive effects of spatial ability and team process on operator workload, while using multiple unmanned vehicles. The hypotheses also focused on how these effects might change when using different measures of spatial ability. In order to examine this, the Guilford-Zimmerman Spatial Visualization and Spatial Orientation scores of an unmanned aerial vehicle (UAV) operator and navigation support provided by this UAV operator to an unmanned ground vehicle (UGV) operator were used as variables predicting UGV operator workload while performing a reconnaissance task. Results indicated that the interaction of the “guider's” spatial visualization and navigation support and the interaction between spatial orientation and navigation support not only accounted for unique variances in the prediction of his/her teammate's workload, but they also produced qualitatively different patterns of results. In identifying these unique contributions, the importance of using multiple spatial ability measures in (unmanned vehicle) research is highlighted.

This paper focuses on the fixed-time leader–follower formation control of multiple underwater vehicles (MUVs) in the presence of external disturbances. First, an adaptive fixed-time disturbance observer (AFxDO) is developed to deal with unknown time-varying environmental disturbances. The developed AFxDO guarantees the fixed-time convergence property of the disturbance observation error and no prior information on the external disturbances or their derivatives is required. Then, with the aid of the developed AFxDO, a distributed event-triggered fixed-time backstepping controller was developed to achieve the leader–follower formation tracking control of MUVs. To solve the “explosion of complexity” problem inherent in the conventional backstepping, a nonlinear filter is introduced to obtain the derivative of the virtual control law. Furthermore, to reduce the communication burden, the event-triggered mechanism is integrated into the formation tracking controller. The stability analysis shows that the closed-loop MUV system is practical fixed-time stable. Finally, simulation results demonstrate the effectiveness of the proposed scheme.

Closed formation control for multiple unmanned ground vehicles (UGVs) is studied in this chapter. The leading-following strategy with a virtual leader is applied to coordinate the whole formation group so that only local information is sufficient for every UGV to maintain close formation. Error-shaping memory-based control is designed for formation path tracking. The salient feature of this approach lies in its simplicity in design and implementation, and no need for detailed information on external disturbances and uncertainties. The performance of the proposed method is verified via real-time experiment on various formation operations.

<p indent="0mm">In the context of multi-unmanned vehicle cooperative encirclement within unknown and uncertain environments, this paper introduces a multi-agent cooperative encirclement decision-making algorithm, BM-MADDPG, based on Bidirectional Long Short-Term Memory (Bi-LSTM) and a Mixed Reward Function. The algorithm is designed to address the challenges of generating encirclement strategies and coordinating control for unmanned vehicles. To begin, the Bi-LSTM network is employed to capture the temporal information features between state and action sequences, enabling an assessment of the long-term effects of different actions in the current state. This addresses the issue of limited information utilization in collaborative encirclement. Furthermore, to overcome the problems associated with sparse rewards, feedback delays, and slow learning convergence in unmanned vehicle encirclement tasks, a Mixed Reward Function that combines sparse and dense rewards is proposed. This mixed reward function guides the encirclement agents to explore, accelerates training convergence, and enhances the collaborative capabilities among multiple unmanned vehicles. Simulations and experimental results reveal that in the context of multi-unmanned vehicle cooperative encirclement, the BM-MADDPG algorithm outperforms MADDPG, achieving a 4.5% increase in encirclement success rate. This effectively enhances the cooperative encirclement capabilities and learning efficiency of multiple unmanned vehicles.

Global fixed-time position-constrained guidance and adaptive fuzzy prescribed performance control using novel shift function for multiple unmanned surface vehicles formation.

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 second-order 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.

This paper addresses the distributed swarm control problem of multiple unmanned surface vehicles (USVs) in Euclidean space with virtual leader. Firstly, to investigate the topology of the neighborhood relations between vehicles, a new time-variant topology structure is proposed. Secondly, to research the dynamic properties of the group for the case where the number of virtual leader is different, a grouping method based on cosine similarity is proposed. Thirdly, to ensure the high effeciency of information transmission and the reduction of costs, a distributed swarm control algorithm is proposed, which is mainly composed of three parts: gradient descent term, velocity consensus term and navigational feedback term. To analyze the stability of system, the concept of translation framework is introduced. Based on the properties of the Hamiltonian and LaSalle invariance principle, the stability of multiple USVs swarm motion is proved. Finally, simulation results illustrate the effectiveness of the proposed methods.

Finite-time sideslip observer-based synchronized path-following control of multiple unmanned underwater vehicles

This paper is concerned with the security sliding mode-based leader-following control of multiple autonomous unmanned underwater vehicles (AUVs) system subject to stochastic DoS attacks. Firstly, a Bernoulli variable is used to describe the phenomenon of stochastic DoS attacks between controllers and actuators, and the attack probability is known for controller design. Secondly, the closed-loop system is established by taking the integral sliding mode control scheme and stochastic DoS attacks into account. Then, the conditions of stochastic stability for system are given by using the stochastic systems analysis method and the model decomposition technique. In the simulation part, a group of AUVs models is used to verify the validity of the proposed state consensus control scheme. The simulation example shows that all the state can achieve consensus even in presence of randomly launched DoS attack.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

In this paper, we present a proof-of-concept demonstration for the electrification of transportation, aiming to create great opportunities for integrating research and education. Multiple unmanned ground electric vehicles are emulated using Lego Mindstorms EV3 education kits. We implement traffic logic (e.g., path tracking algorithm), voice and gesture control, and collision avoidance functionality into each vehicle. Every vehicle is able to communicate with each other and self-navigate on a 240-sqft platform using local measurement (e.g., touch sensor, color sensor, and infrared sensor). Multiple vehicles are used to simulate the real-world traffic scenarios. Two demo-scale plug-in and wireless charging stations are also built up to mimic the dynamic charging for electric vehicles. Meanwhile, each vehicle can wirelessly communicate with the centralized transportation coordinator to find the best charging spot depending on location, user preference, and real-time pricing. In addition, each vehicle is equipped with an on-board battery monitoring system to measure the real-time battery state (e.g., current, voltage, and temperature) and estimate the battery state-of-charge (SOC). The user-friendly graphical interface is developed in Labview for each component of proof-of-concept demonstration. Most importantly, this demonstration is fully compatible with a variety of general purpose engineering software (e.g., Matlab, and Labview), and creates great opportunities for integrating research and education in Smart Grid and intelligent transportation systems.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.