Distributed Adaptive Human-in-the-Loop Event-Triggered Formation Control for QUAVs With Quantized Communication

Guo, Hongzhen; Jiang, Yuhan; Lungu, Mihai

doi:10.1109/tii.2022.3211508

Cited by 15 publications

(5 citation statements)

References 39 publications

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“…Based on the definition of n i and Equation (8), Equation ( 11) can be derived. Additionally, from Formula (13), it can be inferred that ϕ(τ ci ) must be bounded because it is a continuous function, so g i is also bounded.…”

Section: Lemmamentioning

confidence: 99%

“…To cope with these environmental uncertainties, the controller needs to employ robust control algorithms that can adapt to different conditions. As control technology continues to advance and innovate, several advanced control theories, such as sliding mode control (SMC) [5][6][7][8] and back-stepping control [9,10], have been applied to enhance trajectory tracking control for USVs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Ren,

Zhang,

Ying

et al. 2023

JMSE

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This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov’s second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme.

show abstract

Section: Lemmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Ren,

Zhang,

Ying

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Therefore, this can effectively save the communication resources spent via transmission from the controller to the actuator, reduce the number and frequency of the actuator's actions, and achieve the effect of saving energy and protecting the actuator [21]. In recent years, event-trigger mechanisms have been widely used in various control objects: H. G. et al [22] proposed a distributed adaptive human-in-the-loop event-triggered formation controller for quadrotor unmanned aerial vehicles, with limited communication, system uncertainties, and unknown external disturbances in a highly uncertain and safetycritical environment. Aiming at the formation control of ground vehicle formations, a networked system with adaptive nonlinear coupling, a consistent control problem based on the observer, and event-trigger mechanisms were studied [23].…”

Section: Introductionmentioning

confidence: 99%

Event-Triggered Supercavitating Vehicle Terminal Sliding Mode Control Based on Non-Recursive Observation

Zhang,

Wang,

Wang

et al. 2024

JMSE

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Supercavitating vehicles present significant issues in controller design due to their multiphase flow-coupling characteristics. This study addresses force analysis and the construction of a 6-degree-of-freedom mathematical model for a supercavitating vehicle. A terminal sliding mode control law is intended to guarantee the quick tracking of the command signal for high-precision attitude control. To drastically lower the frequency of actuation and communication, a mechanism to trigger events is also introduced into the control link. A disturbance observer, which estimates system uncertainty using a non-recursive differentiator, improves the robustness of the system. The Lyapunov approach is used to prove that the system is stable. Numerical simulation results validate that the proposed method enhances control accuracy and robustness. The event-trigger mechanism reduces the execution frequency to 18.59%, effectively reducing the communication burden.

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“…Reference 17 has designed an event‐triggered finite‐time control protocol for achieving HLC tracking consensus. Furthermore, the event‐triggered control strategy has been utilized in the adaptive formation control of HLC‐based MASs, particularly when dealing with multiple leaders and quantized communication in Reference 18. It is crucial to emphasize that the aforementioned reported HLC distributed tracking control results heavily rely on precise system models.…”

Section: Introductionmentioning

confidence: 99%

Model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems

Mei,

Sun,

et al. 2024

Intl J Robust & Nonlinear

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This article studies the model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems in the presence of an active leader. The core role of human‐in‐the‐loop is to use the ground station to send control commands to the non‐zero control input of the leader, and then directly or indirectly control a group of agents to complete complex tasks. Meanwhile, three essential demands including the completely unknown system model, the control objective obtained optimally, as well as no initial admissible control strategy requirement, are satisfied simultaneously. It is worth emphasizing that the relevant results only satisfy one or two demands at most, which are essentially not applicable to this problem. In this article, a model‐based human‐in‐the‐loop learning algorithm is first presented to achieve the optimal tracking control, as well as the convergence of the proposed learning algorithm is proved. Then, a bias‐based data‐driven learning algorithm is proposed, which provides the potential opportunities to overcome the difficulties caused by the above‐mentioned three demands. Finally, the validity of theoretical results is testified by a numerical example.

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Distributed Adaptive Human-in-the-Loop Event-Triggered Formation Control for QUAVs With Quantized Communication

Cited by 15 publications

References 39 publications

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

Event-Triggered Supercavitating Vehicle Terminal Sliding Mode Control Based on Non-Recursive Observation

Model‐free learning‐based distributed cooperative tracking control of human‐in‐the‐loop multi‐agent systems

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