Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque

Shojaei, Khoshnam

doi:10.1016/j.oceaneng.2015.06.026

Cited by 154 publications

(61 citation statements)

References 18 publications

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“…With regard to USV formation control, Shojaei et al (2015) studied the under-actuated USV formation control under external disturbance and model parameter perturbation. After firstly designing a formation coordination controller based on the leader-follower mode, they utilized state feedback in the design of single USV tracking controller and introduced generalized saturation function to effectively prevent the possible oversaturation caused by actuating mechanism.…”

Section: Usv Controlmentioning

confidence: 99%

“…After that, neural network was utilized to online approach the uncertain terms caused by external disturbance, and eventually Lyapunov stability theory was employed to prove the uniform semi-global stability of closed-loop control system. However, leader's breakdown may lead to the collapse of the formation so that the leader-follower mode is not applicable to practical engineering applications [13]. In 2016, Shojaei further utilized multi-layer neural network and adaptation technology to perform the real-time estimation and compensation for the model perturbation caused by external disturbance on the basis of Reference [13], so as to guarantee the stability of formation controller.…”

Section: Usv Controlmentioning

confidence: 99%

“…However, leader's breakdown may lead to the collapse of the formation so that the leader-follower mode is not applicable to practical engineering applications [13]. In 2016, Shojaei further utilized multi-layer neural network and adaptation technology to perform the real-time estimation and compensation for the model perturbation caused by external disturbance on the basis of Reference [13], so as to guarantee the stability of formation controller. In the end, Lyapunov theory was taken as the basis to prove the uniformly ultimate boundedness for all the states and signals of control system [14].…”

Section: Usv Controlmentioning

confidence: 99%

See 2 more Smart Citations

Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking

Liu

Zhang

2020

Sensors

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Multi-under-actuated unmanned surface vehicles (USV) path tracking control is studied and decoupled by virtue of decentralized control. First, an improved integral line-of-sight guidance strategy is put forward and combined with feedback control to design the path tracking controller and realize the single USV path tracking in the horizontal plane. Second, graph theory is utilized to design the decentralized velocity coordination controller for USV formation, so that multiple USVs could consistently realize the specified formation to the position and velocity of the expected path. Third, cascade system theory and Lyapunov stability are used to respectively prove the uniform semi-global exponential stability of single USV path tracking control system and the global asymptotic stability and uniform local exponential stability of coordinated formation system. At last, simulation and field experiment are conducted to analyze and verify the advancement and effectiveness of the proposed algorithms in this paper.

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Section: Usv Controlmentioning

confidence: 99%

Section: Usv Controlmentioning

confidence: 99%

Section: Usv Controlmentioning

confidence: 99%

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Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking

Liu

Zhang

2020

Sensors

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“…In this paper, a three-layer NN as shown in Figure 2 is introduced to approximate the uncertain terms of WMR in the next design. 11,[40][41][42] The output of NN can be expressed as…”

Section: Three-layer Nnmentioning

confidence: 99%

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

Huang

Zhou

et al. 2018

Intl J Robust & Nonlinear

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This paper presents a robust neural network-based control scheme to deal with the problem of tracking and stabilization simultaneously for a wheeled mobile robot subject to parametric uncertainties, external disturbances, and input saturation. At first, a new error-state transformation scheme is designed by introducing some auxiliary variables as an additional virtual control signals to reduce the adverse effect caused by the underactuation. These variables can change their structures for different desired trajectories to be tracked. Then, a robust control law is proposed combining with a kinematic controller and a dynamic controller, while a three-layer neural network system is applied to approximate model uncertainties. Stability analysis via the Lyapunov theory shows that the proposed controller can make tracking errors converge to bounded neighborhoods of the origin. Finally, some simulation results are illustrated to verify the effectiveness of the proposed control strategy.

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“…A coordinated path following with a switching communication topology was designed in [14], while a null-space-based behavioral control technique was proposed in [15,16]. In [17][18][19], a leader-follower control strategy was presented. In [20], an adaptive coordinate tracking control problem for a fleet of nonholonomic chained systems was discussed under the assumption that the desired trajectory is available only to part of the neighbors.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms

et al. 2018

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Abstract:In this paper, the problem of coordinated control of multiple hovercrafts is addressed. For a single hovercraft, by using the backstepping technique, a nonlinear controller is proposed, where Radial Basis Function Neural Networks (RBFNNs) are adopted to approximate unmodeled terms. Despite the application of RBFNNs, integral terms are introduced, improving the robustness of controller. As a result, global uniformly ultimate boundedness is achieved. Regarding the communication topology, two different directed graphs are chosen under the assumption that there are no delays when they communicate with each other. In order to testify the performance of the proposed strategy, simulation results are presented, showing that vehicles can move forward in a specific formation pattern and RBFNNs are able to approximate unmodeled terms.

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Leader–follower formation control of underactuated autonomous marine surface vehicles with limited torque

Cited by 154 publications

References 18 publications

Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking

Multi-under-Actuated Unmanned Surface Vessel Coordinated Path Tracking

Robust neural network–based tracking control and stabilization of a wheeled mobile robot with input saturation

Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms

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