An adaptive fuzzy sliding mode controller for the depth control of an underactuated underwater vehicle

Chen, Yuan; Yan, Yinpo; Wang, Kangling; Liu, Shuqi

doi:10.1177/1729881419840213

Cited by 4 publications

(3 citation statements)

References 21 publications

(36 reference statements)

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“…The proposed adaptive fuzzy SMC (AFSMC) maintains the formation by coping with the variation in average velocity and lift forces. The FLC algorithm is combined with the SMC to compensate the buffering and chattering issues in the original SMC method (Chen et al, 2019). The required adaptive control law u v ðtÞ can be derived by employing "fixed control allocation scheme" and algebraic theory (Chen et al, 2017) as follows:…”

Section: Stability Analysis Of Afsmcmentioning

confidence: 99%

“…According to the studies, fuzzy logic controllers can be classified as conventional, adaptive and hybrid (Xiang et al, 2018) based on the difference between the fuzzy rules and their generation methods. The application of AFSMC for diving control (Chu et al, 2018), trajectory tracking (Guerrero et al, 2019) and depth control (Chen et al, 2019) of underactuated AUV with input constraints has been found in the literature. In this research, an AFSMC has been proposed to cope with the case of actuator failure subjected to variation in payload mass due to the change in lift forces acting on an AUV surface (see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

Panda

Das

Subudhi

et al. 2020

IJIUS

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PurposeIn this paper, an adaptive fuzzy sliding mode controller (AFSMC) is developed for the formation control of a team of autonomous underwater vehicles (AUVs) subjected to unknown payload mass variations during their mission.Design/methodology/approachA sliding mode controller (SMC) is designed to drive the state trajectories of the AUVs to a switching surface in the state space. The payload mass variation results in parameter variation in AUV dynamics leading to actuator failure. This further leads to loss of communication among the members of the team. Hence, an adaptive SMC based on fuzzy logic is developed to maintain the coordinated motion of AUVs with payload mass variation.FindingsThe results are obtained by employing adaptive SMC for AUVs with and without payload variations and are compared. It is observed that the proposed adaptive SMC exhibits improved performance and tracks the desired trajectory in less time even with variation in the payload. The adaptive fuzzy control algorithm is developed to handle variation in payload mass variation. Lyapunov theory is used to establish stability of AFSMC controller.Research limitations/implicationsPerfect alignment is assumed between centres of gravity (OG) and buoyancy (OB), thus AUVs maintaining horizontal stability during motion. The AUVs’ body centres are aligned with centres of gravity (OG), thus the distance vector being rg = [0,0,0]T. As it is a tracking problem, sway motion cannot be neglected as the AUVs are travelling in a curved locus, hence susceptible to Coriolis and centripetal forces. The AUV is underactuated as only two thrusters at the stern plate that are employed for the surge and yaw controls and error in Y- direction are controlled by adjusting control input in surge and heave direction. Control inputs to the thruster are constants, and depth control is achieved by adjusting the rudder angle.Practical implicationsAUVs are employed in military mission or surveys, and they carry heavy weapons or instrument to be deployed at or picked from specific locations. Such tasks lead to variation in payload, causing overall mass variation during an AUV’s motion. A sudden change in the mass after an AUV release or pick load results in variation in depth and average velocity.Social implicationsThe proposed controller can be useful for military missions for carrying warfare and hydrographic surveys for deploying instruments.Originality/valueA proposed non-linear SMC has been designed, and its performances have been verified in terms of tracking error in X, Y and Z directions. An adaptive fuzzy SMC has been modelled using quantized state information to compensate payload variation. The stability of AFSMC controller is established by using Lyapunov theorem, and reachability of the sliding surface is ensured.

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Section: Stability Analysis Of Afsmcmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

Panda

Das

Subudhi

et al. 2020

IJIUS

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show abstract

“…Considering the robustness to external disturbances, parameter perturbations, and unmodeled dynamics, SMC is an effective and powerful advanced controllers that have been developed considerably in USV and robot areas. [30][31][32][33][34][35] In practical applications, SMC has successfully applied to underactuated biped robot, 36 satellites, 37 and overhead crane. 38 For example, sliding mode observer was designed by Van et al 39 to estimate the robot velocities in the presence of model uncertainties and external disturbances.…”

Section: Introductionmentioning

confidence: 99%

Extended state observer-based integral line-of-sight guidance law for path following of underactuated unmanned surface vehicles with uncertainties and ocean currents

Guo

2021

International Journal of Advanced Robotic Systems

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This article focuses on the problem of path following for underactuated unmanned surface vehicles (USVs) considering model uncertainties and time-varying ocean currents. An extended state observer (ESO)-based integral line-of-sight (ILOS) with an integral sliding mode adaptive fuzzy control scheme is proposed as the main control framework. First, a novel ESO is employed to estimate the surge and sway velocities based on the kinetic model, which are difficult to measure directly. Then, the adaptive ILOS guidance law is proposed, in which the integral vector is incorporated into the adaptive method to estimate the current velocities. Meanwhile, an improved fuzzy algorithm is introduced to optimize the look-ahead distance. Second, the controller is extended to deal with the USV yaw and surge velocity signal tracking using the integral sliding mode technique. The uncertainties of the USV are approximated via the adaptive fuzzy method, and an auxiliary dynamic system is presented to solve the problem of actuator saturation. Then, it is proved that all of the error signals in the closed-loop control system are uniformly ultimately bounded. Finally, a comparative simulation substantiates the availability and superiority of the proposed method for ESO-based ILOS path following of USV.

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A cascaded model predictive control based trajectory tracking controller for an autonomous and remotely operated vehicle with LOS guidance

Xie,

Chen,

Zhu

2023

Ships and Offshore Structures

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An adaptive fuzzy sliding mode controller for the depth control of an underactuated underwater vehicle

Cited by 4 publications

References 21 publications

Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

Adaptive fuzzy sliding mode formation controller for autonomous underwater vehicles with variable payload

Extended state observer-based integral line-of-sight guidance law for path following of underactuated unmanned surface vehicles with uncertainties and ocean currents

A cascaded model predictive control based trajectory tracking controller for an autonomous and remotely operated vehicle with LOS guidance

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