Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

Kang, Song; Rong, Yongfeng; Chou, Wu

doi:10.3390/s20247084

Cited by 23 publications

(8 citation statements)

References 40 publications

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“…In addition, the experiments verify that the controller has strong robustness. Wu et al [16], Wang et al [17] and Kang et al [18] make improvements on the basis of the adaptive controller, which is verified in the field of motion control of AUVs. Zhang et al [19] present a sliding mode variable structure control algorithm, and simulation results show that this algorithm has advantages of high control accuracy and strong robustness.…”

Section: Introductionmentioning

confidence: 84%

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

Meng

Zhang

2021

JMSE

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To achieve rapid and flexible vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle (MJ-AUV) with orthogonal joints was designed. This paper focuses on the 3-dimensional (3D) modeling and attitude control of the designed vehicle. Considering the situation of gravity and buoyancy imbalance, a 3D model of the MJ-AUV was established according to Newton’s second law and torque balance principle. And then the numerical simulation was carried out to verify the credibility of the model. To solve the problems that the pitch and yaw attitude of the MJ-AUV are coupled and the disturbance is unknown, a linear quadratic regulator (LQR) decoupling control method based on a linear extended state observer (LESO) was proposed. The system was decoupled into pitch and yaw subsystems, treated the internal forces and external disturbances of each subsystem as total disturbances, and estimated the total disturbances with LESO. The control law was divided into two parts. The first part was the total disturbance compensator, while the second part was the linear state feedback controller. The simulation results show that the overshoot of the controlled system in the dynamic process is nearly 0 rad, reaching the design value very smoothly. Moreover, when the controlled system is in a stable state, the control precision is within 0.005%.

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Section: Introductionmentioning

confidence: 84%

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

Meng

Zhang

2021

JMSE

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“…An ADRC method based on fuzzy adaptive ESO (extended state observer) was proposed for trajectory tracking of AUV. The simulations indicated that the method outperformed others in terms of tracking accuracy and robustness [10]. A depth ADRC method for AUV was feasible and effective [11].…”

Section: Introductionmentioning

confidence: 91%

“…The motion equations are usually divided into two independent subsystems, the longitudinal one and the lateral one [31]. Omitting the heave, roll, and pitch modes, the motion equations in the horizontal plane are sketched in Equation (10).…”

Section: Motion Equations In the Horizontal Planementioning

confidence: 99%

Motion Control of Autonomous Underwater Vehicle Based on Fractional Calculus Active Disturbance Rejection

Wan

Liu

Yuan

et al. 2021

JMSE

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An active disturbance rejection control based on fractional calculus is proposed to improve the motion performance and robustness of autonomous underwater vehicle (AUV). The active disturbance rejection control (ADRC) method can estimate and compensate the total disturbance of AUV automatically. The fractional-order PID (proportional integral derivative) has fast dynamic response, which can eliminate the estimation error of extended state observer. The fractional calculus active disturbance rejection strategy combines the advantages of the above two algorithms, and it is designed for AUV heading and pitch subsystems. In addition, the stability of fractional calculus ADRC heading subsystem is proven by Lyapunov stability theorem. The numerical simulations and experimental results document that the superior performance has been achieved. The fractional calculus ADRC strategy has more excellent abilities for disturbance rejection, performs better than ADRC and PID, and has important theoretical and practical value.

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“…In this paper, a three-layer feed-forward RBF-NN is used to construct the nonlinear prediction model of Equation (12). The transformation from the input layer to the hidden layer in the NN is nonlinear, while the transformation from the hidden layer to the output layer is linear, and its structure is shown in Figure 3.…”

Section: Design Of Rbf-nn Prediction Modelmentioning

confidence: 99%

Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

Bao,

Zhu,

Liu

2023

Optim Control Appl Methods

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This paper studies the three‐dimensional (3‐D) dynamic trajectory tracking control of an autonomous underwater vehicle (AUV). As AUV is a typical nonlinear system, each degree of freedom is strongly coupled, so the traditional control method based on the nominal model of AUV cannot guarantee the accuracy of the control system. To solve this problem, we first propose a prediction model based on a radial basis function neural network (RBF‐NN). The nonlinearity of AUV is learned and modeled offline by RBF‐NN based on previous data. This model can reflect the time sequence state and control variables of AUV. Secondly, to avoid the overfitting problem in network training based on the traditional gradient descent method, a new adaptive chaotic sparrow search algorithm (ACSSA) is proposed to optimize the network parameters, to improve the full approximation ability of RBF‐NN to nonlinear systems. To eliminate the steady‐state error caused by external interference during AUV trajectory tracking, a nonlinear optimizer is designed by updating the deviation of the NN model output layer. In each sampling period, the predictive control law is calculated online according to the deviation between the predicted value and the actual value. In addition, the stability analysis based on the Lyapunov method proves the asymptotic stability of the controller. Finally, the 3‐D dynamic trajectory tracking the performance of AUV under different external disturbances is verified by MATLAB/Simulink, and the results show that the proposed controller is more efficient and robust than the standard model predictive controller (MPC) controller and the standard NN model predictive controller (NNPC).

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Antidisturbance Control for AUV Trajectory Tracking Based on Fuzzy Adaptive Extended State Observer

Cited by 23 publications

References 40 publications

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

Motion Control of Autonomous Underwater Vehicle Based on Fractional Calculus Active Disturbance Rejection

Improved SSA‐RBF neural network‐based dynamic 3‐D trajectory tracking model predictive control of autonomous underwater vehicles with external disturbances

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