This article aims to develop an effective control method that can improve the convergence rate over the existing adaptive nonsingular integral terminal sliding mode control (ANITSMC) method for the trajectory tracking control of autonomous underwater vehicles (AUVs). To achieve this goal, an adaptive fast nonsingular integral terminal sliding mode control (AFNITSMC) method is proposed. First, considering that the existing nonsingular integral terminal sliding mode (NITSM) has slow convergence rate in the region far from the equilibrium point, a fast NITSM (FNITSM) is proposed, which guarantees fast transient convergence both at a distance from and at a close range of the equilibrium point, and therefore increases the convergence rate over the existing NITSM. Then, using this FNITSM and adaptive technique, an AFNITSMC method is designed for AUVs. It yields local finite-time convergence of the velocity tracking errors to zero and then local exponential convergence of the position tracking errors to zero, without requiring any a priori knowledge of the upper bounds of the uncertainties and disturbances. Compared with the existing ANITSMC method, the salient feature of the proposed AFNITSMC method is that it provides AUV dynamics a faster convergence rate. Finally, simulation results demonstrate the efficiency of the proposed AFNITSMC method and its superiority over the existing ANITSMC method.

Trajectory Tracking Control of AUVs via Adaptive Fast Nonsingular Integral Terminal Sliding Mode Control

/pdf/finite-time-observer-based-tracking-control-of-uncertain-292ifsgi.pdf

Finite-time observer based tracking control of uncertain heterogeneous underwater vehicles using adaptive sliding mode approach

AUV path following controlled by modified Deep Deterministic Policy Gradient

Optimal robust trajectory tracking control of a X-rudder AUV with velocity sensor failures and uncertainties

This article proposes a virtual leader-based coordinated controller for the nonlinear multiple autonomous underwater vehicles (multi-AUVs) with the system uncertainties. To achieve the coordinated formation, a virtual AUV is set as the leader, while the desired command is designed using the relative position between each AUV and the virtual leader. The controller is designed based on the back-stepping scheme, and the online data-based learning scheme is used for uncertainty approximation. The highlight is that compared with previous learning methods which mostly focus on stability, the learning performance index is constructed using the collected online data in this article. The index is further used in the composite update law of the neural weights. The closed-loop system stability is analyzed via the Lyapunov approach. The simulation test on the five AUVs under fixed formation shows that the proposed method can achieve higher tracking performance with improved approximation accuracy.

Virtual Guidance-Based Coordinated Tracking Control of Multi-Autonomous Underwater Vehicles Using Composite Neural Learning.

Composite learning adaptive sliding mode control for AUV target tracking

The antidisturbance control problem of autonomous vehicle path tracking considering lateral stability is studied in this paper. This paper proposes an improved active disturbance rejection control (IADRC) control method including an improved extended state observer (IESO) and an error compensator based on LQR, where a new continuous nonlinear function is proposed in the IESO instead of the classical piecewise function. Based on the IADRC, an autonomous vehicle path-tracking controller considering lateral stability is designed. Using the output wheel steering angle and external yaw moment, the IESO estimates the disturbance value and compensates for the disturbance in the feedback to meet the goal of antidisturbance control. Based on the concept of control allocation (CA), the control distributor is designed to distribute the external yaw moment to the four wheels in a reasonable and optimal way to achieve differential braking. Finally, the control scheme is evaluated in the form of CarSim/Simulink cosimulation; the results show that the proposed autonomous vehicle path-tracking control scheme has better path-tracking effect and higher antidisturbance robustness.

https://www.mdpi.com/2076-3417/12/9/4660/pdf?version=1651828819

Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability

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Composite learning adaptive sliding mode control for AUV target tracking

Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability