Coordinated Formation Control of Discrete-Time Autonomous Underwater Vehicles under Alterable Communication Topology with Time-Varying Delay

Yu, Haomiao; Zeng, Zhenfang; Chen, Guo

doi:10.3390/jmse10060712

Cited by 9 publications

(5 citation statements)

References 37 publications

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“…Given these significant advantages, research on AUV formation control has garnered increasing attention [2]. The primary challenge is to ensure the stability of the formation motion in intricate underwater environments with multiple constraints [3]. Addressing this issue, researchers have undertaken extensive studies in recent years.…”

Section: Introductionmentioning

confidence: 99%

Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints

Yan,

Zhang,

Zhou

et al. 2024

JMSE

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This paper focuses on the formation tracking issue of autonomous underwater vehicles (AUVs) subject to multiple constraints in three-dimensional space. We developed a novel distributed Lyapunov-based model predictive controller (DLMPC) with a fast finite-time extended state observer (FFTESO). Initially, the external disturbances and internal uncertainties of each AUV were precisely compensated using the designed FFTESO. Subsequently, we proposed DLMPC-based position tracking and velocity tracking controllers, which solved an online optimization problem to determine optimal velocities and control forces. This hierarchical framework effectively managed system constraints, such as state constraints and actuator saturation. Additionally, the Lyapunov-based backstepping control law was applied to construct stability constraints in the distributed optimization problem, ensuring the recursive feasibility and closed-loop system stability of the proposed scheme. Sufficient conditions and attraction regions to ensure stability were explicitly provided. Finally, the simulation results demonstrated that the proposed method improved both the convergence speed and tracking accuracy by at least 30% compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints

Yan,

Zhang,

Zhou

et al. 2024

JMSE

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“…Compared with the operational capability of a single AUV, collaborative AUVs can respond more reliably and flexibly to complex missions and extended operational ranges, thereby improving the efficiency and robustness of undersea operations. Given this backdrop, numerous application cases about AUV coordinated formation have been triggered in both civilian and industrial fields for decades [2,3]. Irrespective of the specific collaborative missions undertaken by AUVs, the core challenge lies in ensuring motion stability of AUV formations within complex underwater environments and the constraints of their own models.…”

Section: Introductionmentioning

confidence: 99%

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

Zhang,

Yan,

Zhou

et al. 2023

JMSE

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This paper focuses on the collision-free formation tracking of autonomous underwater vehicles (AUVs) with compound disturbances in complex ocean environments. We propose a novel finite-time extended state observer (FTESO)-based distributed dual closed-loop model predictive control scheme. Initially, a fast FTESO is designed to accurately estimate both model uncertainties and external disturbances for each subsystem. Subsequently, the outer-loop and inner-loop formation controllers are developed by integrating disturbance compensation with distributed model predictive control (DMPC) theory. With full consideration of the input and state constraints, we resolve the local information-based DMPC optimization problem to obtain the control inputs for each AUV, thereby preventing actuator saturation and collisions among AUVs. Moreover, to mitigate the increased computation caused by the control structure, the Laguerre orthogonal function is applied to alleviate the computational burden in time intervals. We also demonstrate the stability of the closed-loop system by applying the terminal state constraint. Finally, based on a connected directed topology, comparative simulations are performed under various control schemes to verify the robustness and superior performance of the proposed scheme.

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“…In the actual application, the multi-AUV formation will inevitably be affected by the actual environment, there will be a communication delay when multi-AUVs communicate with each other, and it takes some time to fuse and calculate the information of each sensor, so the real-time information sharing between multi-AUVs cannot be achieved in the actual application and the multi-AUV formation control will produce large control errors [10,11]. Therefore, the study of multi-AUV formation control under communication delay is helpful to apply the theory to practice and promote the development of multi-AUV formation technology.…”

Section: Introductionmentioning

confidence: 99%

Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints

Tian

Zhang

et al. 2023

JMSE

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For the problem of hydroacoustic communication constraints in multi-AUV leader follower formation, this paper designs a formation control method combining CNN-LSTM prediction and backstepping sliding mode control. First, a feedback linearization method is used to transform the AUV nonlinear model into a second-order integral model; then, the influence of hydroacoustic communication constraints on the multi-AUV formation control problem is analyzed, and a sliding window-based formation prediction control strategy is designed; for the characteristics of AUV motion trajectory with certain temporal order, the CNN-LSTM prediction model is selected to predict the trajectory state of the leader follower and compensate the effect of communication delay on formation control, and combine the backstepping method and sliding mode control to design the formation controller. Finally, the simulation experimental results show that the proposed CNN-LSTM prediction and backstepping sliding mode control can improve the effect of hydroacoustic communication constraints on formation control.

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Coordinated Formation Control of Discrete-Time Autonomous Underwater Vehicles under Alterable Communication Topology with Time-Varying Delay

Cited by 9 publications

References 37 publications

Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints

Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints

Distributed Dual Closed-Loop Model Predictive Formation Control for Collision-Free Multi-AUV System Subject to Compound Disturbances

Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints

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