Backstepping Control Strategy of an Autonomous Underwater Vehicle Based on Probability Gain

Peng, Yudong; Guo, Lanzhong; Meng, Qinghua

doi:10.3390/math10213958

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…In addition to the adaptive control method used for under-actuated AUV, controller design and system stability analysis may suffer from computational difficulty as the increasing of system order. The development of the backstepping algorithm makes the feedback adaptive control systematic and simplifies the complex controller design [29][30][31]. However, the required form of strict feedback is difficult for the under-actuated AUV system.…”

Section: Introductionmentioning

confidence: 99%

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

Li,

Lv,

Lai

et al. 2023

Applied Sciences

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In this paper, an observer-based adaptive control method is investigated for the horizontal trajectory tracking of autonomous underwater vehicles (AUV) with input saturation and system disturbances. Firstly, the desired surge speed and trajectory angle are established, which could decouple the tracking error subsystem and avoid the complex form. Secondly, the input saturation is approximated by a smooth function, and a nonlinear extended states observer (NESO) is designed for estimating system disturbances. Based on the command filtered backstepping technique, which can avoid the explosion caused by the derivative of the virtual control, an observer-based adaptive output feedback control method is developed, and an auxiliary system is applied to compensate for filtered tracking errors, input saturation bias, and observer errors. Finally, simulation results show the proposed method has good robustness in the face of system uncertainties, and the error is nearly 33.3% smaller than that of other control methods when meeting sudden trajectory changes. A good control performance is guaranteed.

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Section: Introductionmentioning

confidence: 99%

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

Li,

Lv,

Lai

et al. 2023

Applied Sciences

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show abstract

“…Due to the model characteristics of AUV (nonlinearity, coupling, uncertainty), there are many external disturbances underwater in practical applications. Most AUVs are underdriven to reduce weight, which degrades the performance of AUVs [6]. The design of the control system becomes one of the most considerable difficulties in its development.…”

Section: Introductionmentioning

confidence: 99%

Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions

Zhang,

Guo,

Sohan

et al. 2023

Applied Sciences

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This paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the AUV movement is affected by the driving force, ocean resistance, and the force generated by the water current, the expected output of the AUV’s system is difficult to meet the expectations, making the AUV trajectory tracking problems challenging. There are two main options for finding suitable controllers for AUVs. The first is making the AUV model achieve better stability using a more complex controller. The second is the simpler controller structure, which can ensure faster system feedback. The RBF and model reference adaptive control (MEAC) system are combined to increase the number of hidden layers, increasing the AUV tracking stability. Because the embedded computing module of an AUV is a bit limited, 31 hidden layers are chosen to simplify the controller structures. A couple of Lyapunov functions are designed for the expected surge and sway velocities, and the vehicle tracking error gradually converges to (0,0). The controller design results are imported into the AUV actuator model by software, and after 0.64 s, the AUV tracking error is less than 1%. At last, the vehicle tracking experiments were carried out, showing that after 0.5 s, the AUV tracking error was less than 1%.

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Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

Wang

et al. 2023

Ocean Engineering

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Backstepping Control Strategy of an Autonomous Underwater Vehicle Based on Probability Gain

Cited by 4 publications

References 34 publications

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

Observer-Based Adaptive Control for Trajectory Tracking of AUVs with Input Saturation

Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions

Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

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