Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation

Liu, Siyuan; Liu, Yancheng; Liang, Xiaoling; Wang, Weining

doi:10.1007/s11071-019-05279-w

Cited by 24 publications

(14 citation statements)

References 41 publications

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“…Following the argument from Kumar et al, 17 we conclude that (I À M À1 M RB ) are real positive, and all its eigenvalues are within the unit disk. Since Eig (J(I À M À1 M RB ) J À1 ) = Eig (I À M À1 M RB ), condition (30) is satisfied, and therefore, (K P e + K D _ e) + e(t) is bounded proving the BIBO stability of the presented control law. Then, from equations ( 8) and ( 12) we get…”

Section: Symbol Description L the Time Delay In Tde Hmentioning

confidence: 81%

“…Nevertheless, both these techniques under-perform in terms of AUV position tracking under large system dynamics uncertainty. Recently, Liu et al 30 considered Gaussian noises and suggested an uncertain observation-based adaptive fuzzy neural dynamic surface control (UOB-AFNDSC) dealing with unknown system uncertainties and input saturation. UOB-AFNDSC relies on the dynamic surface technique that exploits an online succinct fuzzy-neuro uncertainty observer to identify the system uncertainties and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

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Position control for an autonomous underwater vehicle under noisy conditions

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Position tracking of an autonomous underwater vehicle is not trivial as its accuracy is affected by process noise, measurement noise, and uncertain hydrodynamic parameters. Thus, in this article, we studied the position control of an autonomous underwater vehicle that operates under largely unbounded system uncertainties and large noise levels and proposed a method that reduces the interference and thereby enhances the overall autonomous underwater vehicle position estimation. Our technique extended the ensemble Kalman filter by combining it with a time-delay estimator to compensate against extended uncertainty of the system dynamics which cannot be solved by the traditional ensemble Kalman filter. Our synthetic simulations demonstrated the effectiveness of the proposed controller, highlighting its appealing position-control accuracy under simultaneous noise and uncertain hydrodynamics parameters. In addition, our simulation results showed that the proposed controller outperforms the conventional time-delay controller by a percentage range of approximately 30.8%–92.6% in terms of root-mean-square error and requires on average less than 88.2% calculation time than the conventional model predictive control.

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Section: Symbol Description L the Time Delay In Tde Hmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Position control for an autonomous underwater vehicle under noisy conditions

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…Observer‐based control strategy is also one option to design controller for underwater vehicles, where an observer is used to reconstruct the external disturbances or other uncertainties. For instance, an observer‐based dynamic surface control was provided for a fully actuated underwater vehicle, where the lumped uncertainty term was reconstructed by a fuzzy‐neuro observer in [11]. Among these existing control strategies in underwater vehicles, sliding mode control or its variant has been widely applied.…”

Section: Introductionmentioning

confidence: 99%

Virtual tracking control of underwater vehicles based on error injection and adaptive gain

Liu

Zhang

Yao

et al. 2021

IET Control Theory & Appl

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An improved virtual tracking control scheme is proposed based on error injection and adaptive gain for underwater vehicles in the presence of a large initial tracking error and external disturbances. To relieve the effect caused by a large initial tracking error, the developed control scheme is achieved based on two closed‐loop systems. Specifically, a virtual closed‐loop system is constructed based on an approximate dynamic model of an underwater vehicle, while an actual closed‐loop system is built with a real underwater vehicle. Firstly, in order to improve the tracking precision of the virtual tracking control scheme, an auxiliary variable produced by a first‐order filter is injected into a virtual tracking error in the virtual closed‐loop system. And then, the virtual trajectory provided by the virtual closed‐loop system is followed by the actual closed‐loop system. In the actual closed‐loop system, a modified sliding mode surface is designed to achieve the finite‐time stability, while the control gains can be on‐line adjusted based on the tracking performance. Finally, the effectiveness and feasibility of the proposed control scheme are demonstrated by case studies on an underwater vehicle subject to different external disturbances.

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“…In addition, ignoring the unknown disturbances in controller design, the precision and robustness will have an effect on the performance of the closed-loop system. Neural network has the ability to adjust the parameters adaptively [21]. Comparing with the existing work, modelbased control [22] and the linearity-in-parameter assumption [23,24] are not required.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Platoon Control for Marine Surface Vehicles in the Presence of Constraints

Liang

Wang

2020

Complexity

Self Cite

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This paper presents the platoon formation control design for unmanned surface vehicles with input and output constraints. First, Barrier Lyapunov Function (BLF) is employed to ensure the desired line-of-sight (LOS) range. The LOS range should be maintained within the predefined regions between each marine vehicle and its predecessor while the connected platoons track the trajectory. Next, to handle the model uncertainties and unknown external disturbance, we propose an adaptive neural network controller that approximates the unknown nonlinearities. Furthermore, the Nussbaum function is applied to compensate for input saturations. In addition, formation errors can be guaranteed by stability analysis. The distance between two consecutive agents does not violate collision avoidance and connectivity in the presence of input saturation. Finally, the feasibility of the theoretical results is illustrated through the simulation results.

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Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation

Cited by 24 publications

References 41 publications

Position control for an autonomous underwater vehicle under noisy conditions

Position control for an autonomous underwater vehicle under noisy conditions

Virtual tracking control of underwater vehicles based on error injection and adaptive gain

Adaptive Platoon Control for Marine Surface Vehicles in the Presence of Constraints

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