Discrete-time quasi-sliding mode control of an autonomous underwater vehicle

Lee, Pan-Mook; Hong, Seok-Won; Lim, Yong‐Kon; Lee, Chong-Moo; Jeon, Bong-Hwan; Park, Jong-Won

doi:10.1109/48.775300

Cited by 71 publications

(4 citation statements)

References 12 publications

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“…However, nonlinear control algorithms are more suitable for AUVs due to their highly nonlinear dynamics and timevarying external disturbance, as well as uncertainties in the hydrodynamic coefficients. The nonlinear control methods usually used for AUVs are feedback linearization [5]， backstepping control [6], sliding mode control [7], fuzzy control [8] and neural network control [9]. However, each control method, when used alone, has certain flaws and limitations.…”

Section: Introductionmentioning

confidence: 99%

Robust Fixed-Time 3D Trajectory Tracking Control for Underactuated AUVs Based on a Fixed-Time SSNN Disturbance Observer

Liu,

Feng,

Tian

et al. 2024

IEEE Access

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For the problem of accurate tracking control of underactuated autonomous underwater vehicles (AUVs), a robust fixed-time control strategy is proposed to make AUVs converge to the desired reference trajectory in a fixed time. First, a new asymmetric barrier function is proposed to improve the tracking accuracy by constraining the errors to a defined range. Second, a fixed-time self-structuring neural network disturbance observer is proposed to estimate the external disturbances and guarantee that the observer errors are fixed-time convergent. The number of neurons can be optimized to reduce the computational burden. Third, a nonlinear first-order filter is employed to solve the effect of "complexity explosion" in the backstepping control method. Finally, the input quantizer is employed to reduce chattering and updating frequency on the premise of ensuring control accuracy. It is proven that the tracking error of the AUV can converge to the residual set in a fixed time, and the simulation results not only verify the validity of the method but also demonstrate the superiority of the method.INDEX TERMS Fixed-time control, self-structuring neural network disturbance observer, input quantization, underactuated autonomous underwater vehicles, 3D-trajectory tracking.

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Section: Introductionmentioning

confidence: 99%

Robust Fixed-Time 3D Trajectory Tracking Control for Underactuated AUVs Based on a Fixed-Time SSNN Disturbance Observer

Liu,

Feng,

Tian

et al. 2024

IEEE Access

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“…Elmokadem et al proposed the use of sliding mode control to control AUV trajectory tracking in the horizontal plane, considering a general reference trajectory [11]. For situations with uncertain parameters and longer sampling times, Lee et al [12] designed a discrete-type sliding mode variable structure controller for UUVs based on sliding mode variable structure theory and achieved good results. Bian et al [13] utilized the virtual guide idea to establish the UUV vertical plane trajectory tracking error equation for the design of a sliding mode incremental feedback controller.…”

Section: Introductionmentioning

confidence: 99%

Depth control of an unmanned underwater vehicle based on robust inversion and sliding mode variable structure control

Li,

Huang,

Chen

et al. 2023

J. Phys.: Conf. Ser.

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This paper addresses the depth control problem of an underactuated Unmanned Underwater Vehicle (UUV) subject to model uncertainties and ocean current interference. A robust inversion sliding mode variable structure control method is proposed. First, a vertical-plane UUV depth control model is established. Then, the inversion control method is combined with the sliding mode variable structure control method to devise an inversion sliding mode control algorithm. In order to further improve the control system’s robustness against external environmental interference, a robust control methodology is introduced for the design of the depth controller. Simulation results demonstrate that the designed controller effectively addresses the issues of model uncertainties and ocean current interference, thereby enhancing the UUV’s depth control accuracy.

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“…As an effective robust control method, sliding mode control (SMC) has been extensively studied [1][2][3][4][5][6]. This method has been applied in marine systems [7,8], electromechanical systems [9,10], energy systems [11,12], Markov jump systems [13,14], multiagent systems [15], and so on. In general, the design process of SMC comprises two phases: (a) designing a proper sliding surface with desired state trajectory; (b) designing controller to drive the system state trajectory to reach and stay on the sliding surface.…”

Section: Introductionmentioning

confidence: 99%

An adaptive reaching law of chattering‐free discrete‐time sliding mode control for systems with external disturbance

Deng

Zhang

2022

Asian Journal of Control

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An adaptive reaching law of chattering‐free discrete‐time sliding mode control is proposed for the systems with external disturbance. An adaptive function is proposed in the reaching law which can increase reaching speed as sliding variable is far away from zero and avoid undesired chattering when sliding variable is close to origin. Moreover, a third‐order backward difference of disturbance is added to obtain a narrower quasi‐sliding mode band (QSMB). Both the closed‐loop system performance and finite reaching steps to achieve the desired QSMB are theoretically analyzed. The simulation results verify the effectiveness of the proposed control strategy.

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Discrete-time quasi-sliding mode control of an autonomous underwater vehicle

Cited by 71 publications

References 12 publications

Robust Fixed-Time 3D Trajectory Tracking Control for Underactuated AUVs Based on a Fixed-Time SSNN Disturbance Observer

Robust Fixed-Time 3D Trajectory Tracking Control for Underactuated AUVs Based on a Fixed-Time SSNN Disturbance Observer

Depth control of an unmanned underwater vehicle based on robust inversion and sliding mode variable structure control

An adaptive reaching law of chattering‐free discrete‐time sliding mode control for systems with external disturbance

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