Cooperative formation control of autonomous underwater vehicles: An overview

Das, Bikramaditya; Subudhi, Bidyadhar; Pati, Bibhuti Bhusan

doi:10.1007/s11633-016-1004-4

Cited by 158 publications

(59 citation statements)

References 123 publications

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“…e nonlinear system (23) can be transformed into the linearization system if the following conditions can be satisfied: (1)…”

Section: Five Degrees Of Freedom Uuv Modelsmentioning

confidence: 99%

“…e UUV is also known as an autonomous underwater vehicle (AUV). e UUV and AUV are autonomous, which means they perform the assigned mission without any human intercession [1]. Recently, the cooperative control of the multi-UUVs has been becoming a hot topic in various applications, including ocean exploration, submarine rescue, minesweeping, and other fields [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

Yan

Liu³

et al. 2020

Complexity

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We propose a consensus control strategy for multiple unmanned underwater vehicles (multi-UUVs) with unmeasurable disturbances under the fixed and switching topologies. The current methods presented in the literature mainly solve tracking consensus problems with the disturbances under the time-invariant and time-varying topologies, respectively. In the paper, considering the complex nonlinear and couple model of the UUV, the technique of the feedback linearization is employed to transform the nonlinear UUV model into a second-order integral UUV model. For unmeasurable disturbances consisting of unknown model uncertainties and external disturbance for each UUV, we design the distributed extended state observer (DESO) to estimate the disturbances using the UUV position information relative to its neighbours. Moreover, leader-following multi-UUVs consensus control algorithm that enables all following UUVs to track the leader UUV state information based on the estimation state information from the DESO is proposed for two types of topologies, the fixed and switching topologies. Finally, simulation results are shown to demonstrate the effectiveness of the algorithm proposed in the paper.

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“…e nonlinear system (23) can be transformed into the linearization system if the following conditions can be satisfied: (1)…”

Section: Five Degrees Of Freedom Uuv Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

Yan

Liu³

et al. 2020

Complexity

View full text Add to dashboard Cite

show abstract

“…Co-operative coordination of AUVs in acoustics medium is subjected to a variety of constraints depending upon practical implications such as control design for team leader, follower AUVs and communication topology such that full observation can be achieved (Das et al 2016a). The problem of coordination and control of multiple AUVs in the presence of obstacles depends on layered control strategies and the stability of geometry formation in a heterogeneous environment.…”

Section: Problem Formulationmentioning

confidence: 99%

Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Das

Subudhi

Pati

2016

Journal of Marine Engineering & Technology

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This paper presents the cooperative control of a team of autonomous underwater vehicles (AUVs) in the presence of obstacles under environmental disturbance. A leader-follower formation control based on optimisation algorithms using communication topology is designed to navigate towards the target in the presence of obstacles. CLONAL selection optimisation algorithm may be employed as team controller by providing the optimal position to hierarchical control strategy for each AUV incorporating learning, memory and affinity. But unfortunately CLONAL selection optimisation algorithm fails to avoid obstacles because some of the lymphocytes become memory cells of the system. The above prematurity may be overcome by using artificial potential fields and ant colony optimisation technique combined with CLONAL selection optimisation algorithm. The efficacy of the proposed optimisation is verified through MATLAB simulation and the result confirmed the robustness and proficiency of proposed technique over CLONAL selection optimisation algorithm.

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“…Hence, it is meaningful to develop intelligent control methods for AUVs to move in an unmanned way. And two recent survey papers This [1], [2] are recommended for the latest developments in the design and motion control of AUVs. However, many offshore applications of AUVs, such as trajectory tracking problem, are very challenging due to three main difficulties.…”

Section: Introductionmentioning

confidence: 99%

Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

Shi

Song

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively. Specifically, for the critics, the expected absolute Bellman error based updating rule is used to choose the worst critic to be updated in each time step. Subsequently, to calculate the loss function with more accurate target value for the chosen critic, Pseudo Q-learning, which uses sub-greedy policy to replace the greedy policy in Q-learning, is developed for continuous action spaces, and Multi Pseudo Q-learning (MPQ) is proposed to reduce the overestimation of action-value function and to stabilize the learning. As for the actors, deterministic policy gradient is applied to update the weights, and the final learned policy is defined as the average of all actors to avoid large but bad updates. Moreover, the stability analysis of the learning is given qualitatively. The effectiveness and generality of the proposed MPQ-based Deterministic Policy Gradient (MPQ-DPG) algorithm are verified by the application on AUV with two different reference trajectories. And the results demonstrate high-level tracking control accuracy and stable learning of MPQ-DPG. Besides, the results also validate that increasing the number of the actors and critics will further improve the performance.

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Cooperative formation control of autonomous underwater vehicles: An overview

Cited by 158 publications

References 123 publications

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

Co-operative control of a team of autonomous underwater vehicles in an obstacle-rich environment

Multi Pseudo Q-Learning-Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

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