A robust model predictive path following controller for an Autonomous Underwater Vehicle

Rath, Biranchi Narayan; Subudhi, Bidyadhar

doi:10.1016/j.oceaneng.2021.110265

Cited by 15 publications

(6 citation statements)

References 25 publications

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“…In the case of HROV, Scilab is quite a good tool for the validation and simulation of advanced control systems. In Rath and Subudhi [15], a model predictive control approach is applied to the path‐following controller of an AUV, in which the performance in tracking is better compared to the conventional

{{\rm{ {\mathcal H} }}}_{\infty }

control in state feedback. The synthesis methodology presented here is an alternative for designing advanced controllers and the results showed robustness in performance and stability.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…There are also nonlinear control approaches that may be applied, such as backstepping and sliding mode [3]. However, linear robust control is still one of the best alternatives to be considered in the analysis and design of controllers for unmanned vehicles [15]. The advances in control systems are led by the aerospace industry due to guarantee improved performance and stability specifications.…”

Section: Introductionmentioning

confidence: 99%

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Robust control design on Scilab: Hybrid remotely operated vehicle

Roque‐Quispe,

Alvarez‐Quispe,

Yanyachi

et al. 2023

Comp Applic In Engineering

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The goal of this work is to demonstrate the advanced features of Scilab/Xcos software in the process of designing robust controllers and to validate them numerically by a multivariable system with high coupled dynamics; it is a hybrid remotely operated vehicle (HROV). The methodology presents five steps which are the nonlinear model representation, the model linearization, the building of augmented plants, the synthesis of robust controllers through the optimization algorithms, and the plotting of the results. Linear models can be obtained straightforwardly at many operation points. The robust controllers are synthesized using the mixed sensitivity approach and tested in Scilab script and in the Xcos block diagram interface. The surge and yaw rate vehicle dynamics are controlled by a centralized controller; obtaining a good settling time, of less than 14 s, without overshooting and mitigating the coupling dynamics. Moreover, the good shaping in sensitivity and complementary sensitivity indicates robustness in performance and stability for the HROV. The proposed methodology includes time and frequency domain analysis, among other advanced features, to overcome the need for commercial software.

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{{\rm{ {\mathcal H} }}}_{\infty }

control in state feedback. The synthesis methodology presented here is an alternative for designing advanced controllers and the results showed robustness in performance and stability.…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust control design on Scilab: Hybrid remotely operated vehicle

Roque‐Quispe,

Alvarez‐Quispe,

Yanyachi

et al. 2023

Comp Applic In Engineering

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“…Autonomous underwater vehicle (AUV) has shown its irreplaceable role in diversified marine missions, such as underwater detection, pipeline inspection, and ocean reconnaissance 1–3 . During these mission, the AUVs are always expected to maneuver along a predefined path without any temporal requirements, which is known as path following problem 3–5 . Note that the commonly‐used AUVs employ torpedo‐shaped underactuated configuration and have no direct forces in the sway and heave directions 6 .…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] During these mission, the AUVs are always expected to maneuver along a predefined path without any temporal requirements, which is known as path following problem. [3][4][5] Note that the commonly-used AUVs employ torpedo-shaped underactuated configuration and have no direct forces in the sway and heave directions. 6 It is therefore a challenging work to design robust path following controller for underactuated AUVs, especially in the presence of unpredictable environmental disturbances and parameter perturbations.…”

mentioning

confidence: 99%

Three‐dimensional path following control of underactuated autonomous underwater vehicles with nonzero roll dynamics: A novel line‐of‐sight‐guided approach

Zhang,

Li,

Wang

et al. 2024

Intl J Robust & Nonlinear

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Most existing path following control approaches for underactuated Autonomous underwater vehicles (AUVs) assumed that the roll angle can be passively stabilized at zero, and simplified the problem to the 5‐degree‐of‐freedom model. However, in practice, the roll motion suffers from undesirable oscillations induced by environmental disturbances, unbalanced propeller torque, etc. To preserve the stability of the system under nonzero roll dynamics, this paper proposes a novel line‐of‐sight‐ (LOS) guided path following control approach. Firstly, the spatial path following error dynamics model is derived to comprehensively reveal the negative effect of roll dynamics. Secondly, an improved LOS guidance law is designed to compensate for the nonzero roll angle. A salient feature is that the proposed guidance law can be applied to the underwater vehicle no matter it is underactuated in the roll motion or not. Thirdly, by assigning the guidance angles to the desired attitude angles, a robust attitude tracking controller is developed, in which the adaptive estimation technique is employed to handle the lumped uncertainties. The tanh‐based saturation function is incorporated such that the resultant control signals are inherently bounded. The closed‐loop path following and attitude tracking errors are proved to be globally asymptotically stable at the origin. Comparative simulation results are provided to substantiate the effectiveness and superiority of the proposed method.

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“…Wang et al studied the problem of path tracking at Autonomous Ground Vehicles (AGVs) in the presence of sideslip angles [6]. Rath et al proposed a robust model predictive controller for accomplishing an effective path following motion plan of an Autonomous Underwater Vehicle in face of uncertainties, external disturbances, and actuator constraints [7]. Murillo et al presented a novel non-linear mathematical model of an articulated tractor-trailer system [8].…”

Section: Introductionmentioning

confidence: 99%

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

Han,

Liu,

Peng

2024

J. vibroeng.

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In order to solve the problem of accurate vehicle path tracking and address the issues of low convergence accuracy and susceptibility to local optima in Whale Optimization Algorithm (WOA), a nonlinear convergence factor is proposed and nonlinear inertia weights are introduced to improve the basic WOA. Firstly, the convergence factor in WOA is changed to a nonlinear convergence factor, and a nonlinear inertia weight is introduced to improve the convergence accuracy, local development ability, and global search ability. Then, this algorithm is combined with a fifth-degree polynomial. The simulation results show that the proposed method can solve the problem of vehicle path tracking effectively. And also, the vehicle can track the given path controlled by the proposed algorithm with higher accuracy and has stronger applicability. The study can help drivers easily identify safe lane-changing trajectories and area.

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A robust model predictive path following controller for an Autonomous Underwater Vehicle

Cited by 15 publications

References 25 publications

Robust control design on Scilab: Hybrid remotely operated vehicle

Robust control design on Scilab: Hybrid remotely operated vehicle

Three‐dimensional path following control of underactuated autonomous underwater vehicles with nonzero roll dynamics: A novel line‐of‐sight‐guided approach

Optimal control of autonomous vehicle path tracking based on whale optimization algorithm

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