Control Of Autonomous Underwater Vehicle Research Articles

Adaptive reward shaping based reinforcement learning for docking control of autonomous underwater vehicles

Adaptive reward shaping based reinforcement learning for docking control of autonomous underwater vehicles

Three-dimensional path-following control of nonlinear autonomous underwater vehicles with actuator saturation

Three-dimensional path-following control of nonlinear autonomous underwater vehicles with actuator saturation

Finite-Time Path-Following Control of Underactuated AUVs with Actuator Limits Using Disturbance Observer-Based Backstepping Control

This paper presents a three-dimensional (3D) robust adaptive finite-time path-following controller for underactuated Autonomous Underwater Vehicles (AUVs), addressing model uncertainties, external disturbances, and actuator magnitude and rate saturations. A path-following error system is built in a path frame using the virtual guidance method. The proposed cascaded closed-loop control scheme can be described in two separate steps: (1) A kinematic law based on a finite-time backstepping control (FTBSC) is introduced to transform the 3D path-following position errors into the command velocities; (2) The actual control inputs are designed in the dynamic controller using an adaptive fixed-time disturbance observer (AFTDO)-based FTBSC to stabilize the velocity tracking errors. Moreover, the adverse effects of magnitude and rate saturations are reduced by an auxiliary compensation system. A Lyapunov-based stability analysis proves that the path-following errors converge to an arbitrarily small region around zero within a finite time. Comparative simulations illustrate the effectiveness and robustness of the proposed controller.

Safety-Critical Trajectory Tracking Control with Safety-Enhanced Reinforcement Learning for Autonomous Underwater Vehicle

This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance.

Low complexity fault-tolerant formation control for autonomous underwater vehicles with global prescribed-time performance

This paper investigates a novel control scheme for multiple autonomous underwater vehicle (AUV) systems in the presence of measurement errors, unknown external disturbances, and actuator faults. The proposed strategy achieves robust formation control while ensuring global prescribed-time performance. By employing passive strategies and event-triggering mechanism, the communication burden between controller and actuator and computational complexity are significantly reduced. All closed-loop signals are proven to be bounded, and Zeno behavior is avoided. The effectiveness of this control scheme is validated through a simulation.

Consensus-Based Formation Control and Gyroscopic Obstacle Avoidance for Multiple Autonomous Underwater Vehicles on SE(3)

To address the control challenges posed by increasingly complex mission scenarios, this paper aims to develop an advanced formation control and obstacle avoidance strategy for autonomous underwater vehicles (AUVs) in SE(3). This study establishes a dynamic model for fully actuated AUVs and designs a consensus-based formation control strategy to achieve coordinated movement. Motivated by limitations of existing obstacle avoidance strategies such as local minima issues and mutual interference between formation members in high-density environments, this paper introduces a novel gyroscopic force-based obstacle avoidance method. The proposed approach leverages the principles of rotation and angular momentum conservation to enable effective obstacle avoidance while maintaining formation integrity. Simulation results demonstrate the effectiveness of the proposed methodology in achieving robust formation control and collision avoidance under challenging conditions.

Dynamic Boundary Estimation of Suspended Sediment Plume Benefit by the Autonomous Underwater Vehicle Sensing.

The suspended sediment plume generated in the deep-sea mining process significantly impacts the marine environment and seabed ecosystem. Accurate boundary estimation can effectively monitor the scope of environmental impact, guiding mining operations to prevent ecological damage. In this paper, we propose a dynamic boundary estimation approach for the suspended sediment plume, leveraging the sensing capability of the Autonomous Underwater Vehicles (AUVs). Based on the plume model and the point-by-point sensor measurements, a Luenberger-type observer is established for designing the AUV control algorithm. To address the challenge of unknown and time-varying environmental parameters, the estimation errors are reduced by using the projection modification unit. Rigorous convergence and stability analyses of the proposed control algorithm are provided by the Lyapunov method. Numerical simulations demonstrate that the improved algorithm enhances the estimation accuracy of unknown parameters and enables the AUV to patrol along the dynamic boundary in a shorter time, thereby verifying the effectiveness of the boundary estimation algorithm based on AUV sensing.

Rotation matrix-based finite-time trajectory tracking control of benthic AUV with communication bandwidth limitation

The aim of this study was to address the trajectory tracking control problem of benthic autonomous underwater vehicles (AUVs) subjected to the unwinding phenomenon, lumped uncertainties, and communication bandwidth limitations. The hydrodynamic model of the AUV expressed using the Euler angle exhibits an unwinding phenomenon, and the attitude angle cannot be uniquely represented. Hence, the attitude dynamics model is restructured using a rotation matrix to solve this problem. Because of the growing complexity of the controller design for employing a rotation matrix representation for the attitude model, it is essential to adopt an alternative error format that facilitates simplified calculations. Subsequently, a new singularity-free sliding mode control law is proposed to satisfy finite-time convergence. Furthermore, a bounded adaptive law is introduced to enhance the robustness under lumped uncertainties. Additionally, an event-triggering mechanism is implemented for the control laws to alleviate the communication burden on the AUV control system. Theoretical analysis and simulations demonstrate the effectiveness and superiority of the proposed method.

Three-Dimensional Path Following Control for Underactuated AUV Based on Ocean Current Observer

In the marine environment, the motion characteristics of Autonomous Underwater Vehicles (AUVs) are influenced by unknown factors such as time-varying ocean currents, thereby amplifying the complexity involved in the design of path-following controllers. In this study, a backstepping sliding mode control method based on a current observer and nonlinear disturbance observer (NDO) has been developed, addressing the 3D path-following issue for AUVs operating in the ocean environment. Accounting for uncertainties like variable ocean currents, this research establishes the AUV’s kinematics and dynamics models and formulates the tracking error within the Frenet–Serret coordinate system. The kinematic controller is designed through the line-of-sight method and the backstepping method, and the dynamic controller is developed using the nonlinear disturbance observer and the integral sliding mode control method. Furthermore, an ocean current observer is developed for the real-time estimation of current velocities, thereby mitigating the effects of ocean currents on navigational performance. Theoretical analysis confirms the system’s asymptotic stability, while numerical simulation attests to the proposed method’s efficacy and robustness in 3D path following.

Distributed Adaptive Fuzzy 3-D Formation Tracking Control of Underactuated Autonomous Underwater Vehicles

Distributed Adaptive Fuzzy 3-D Formation Tracking Control of Underactuated Autonomous Underwater Vehicles

Adaptive Asymptotic Tracking Control for Underactuated Autonomous Underwater Vehicles With State Constraints

Adaptive Asymptotic Tracking Control for Underactuated Autonomous Underwater Vehicles With State Constraints

Learning‐based tracking control of AUV: Mixed policy improvement and game‐based disturbance rejection

AbstractA mixed adaptive dynamic programming (ADP) scheme based on zero‐sum game theory is developed to address optimal control problems of autonomous underwater vehicle (AUV) systems subject to disturbances and safe constraints. By combining prior dynamic knowledge and actual sampled data, the proposed approach effectively mitigates the defect caused by the inaccurate dynamic model and significantly improves the training speed of the ADP algorithm. Initially, the dataset is enriched with sufficient reference data collected based on a nominal model without considering modelling bias. Also, the control object interacts with the real environment and continuously gathers adequate sampled data in the dataset. To comprehensively leverage the advantages of model‐based and model‐free methods during training, an adaptive tuning factor is introduced based on the dataset that possesses model‐referenced information and conforms to the distribution of the real‐world environment, which balances the influence of model‐based control law and data‐driven policy gradient on the direction of policy improvement. As a result, the proposed approach accelerates the learning speed compared to data‐driven methods, concurrently also enhancing the tracking performance in comparison to model‐based control methods. Moreover, the optimal control problem under disturbances is formulated as a zero‐sum game, and the actor‐critic‐disturbance framework is introduced to approximate the optimal control input, cost function, and disturbance policy, respectively. Furthermore, the convergence property of the proposed algorithm based on the value iteration method is analysed. Finally, an example of AUV path following based on the improved line‐of‐sight guidance is presented to demonstrate the effectiveness of the proposed method.

Event-Triggered Two-Part Separation Control of Multiple Autonomous Underwater Vehicles Based on Extended Observer

In this paper, we investigate the formation isolation regulation issue regarding multiple Autonomous Underwater Vehicles (AUVs) characterized by a “leader–follower” framework. Considering the cooperative–competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer is designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme is designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from Zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source, and the structure convergence is achieved, with the absence of Zeno behavior also demonstrated.

Adaptive hierarchical interval type-2 fuzzy control for trajectory tracking of an underactuated AUV with uncertain dynamics

In this study, an adaptive hierarchical interval type-2 fuzzy control (AHIT2FC) scheme is presented for three-dimensional (3D) trajectory tracking control of an underactuated autonomous underwater vehicle (AUV), which has only three independent control inputs and is subject to partially parametric uncertainty and unknown external disturbances. To decrease reliance on hydrodynamic parameters, an interval type-2 fuzzy logic system (IT2FLS) is utilized, which exhibits superior capabilities in handling high levels of uncertainties prevalent in the underwater environment as compared to a type-1 fuzzy logic system. However, the traditional fuzzy system structure encounters the “curse of dimensionality” issue, which results in high computational complexity and imposes a significant burden on the limited computing resources underwater. The proposed AHIT2FC framework, based on a hierarchical structure, can substantially enhance computational speed while ensuring excellent control performance. Subsequently, Lyapunov’s theory is adopted to guarantee that all the tracking errors in the closed-loop system are semiglobally uniformly bounded (SGUB). Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed AHIT2FC scheme.

Arbitrary 6-DOF large-angle tracking control for autonomous underwater vehicles via stochastic contraction stability and rotation matrix-based attitude representation

Arbitrary 6-DOF large-angle tracking control for autonomous underwater vehicles via stochastic contraction stability and rotation matrix-based attitude representation

Dynamic Docking Anti‐Disturbance Control of Overactuated AUV: System, Method, and Lake Trails

ABSTRACTDynamic docking control technology is crucial for autonomous underwater vehicles (AUV) to perform tasks underwater. To enhance the docking success rate of AUVs during dynamic docking, this paper presents a robust anti‐disturbance control algorithm specifically designed for overactuated AUV dynamic docking scenarios. During a dynamic docking mission, the AUV's depth control is adversely affected by the complex flow field generated by the underwater recovery device. To address this issue, this research proposes an AUV control scheme that combines an extended state observer (ESO) with a combined disturbance rejection method of the elevator‐vertical tunnel controller. First, an ESO is constructed to estimate and compensate for complicated disturbances such as model uncertainty and environmental disturbances. These estimations are then incorporated into the control law to mitigate the effects of the complicated flow field interference experienced during the AUV's dynamic docking process. Second, as turbulence intensifies at the end of the docking stage, the vertical thrust allocation is achieved using a hyperbolic tangent transition function. This ensures the stability of the AUV's attitude and depth, thereby enabling precise docking. Finally, the effectiveness of the proposed control algorithm is verified through lake trials and compared against the classic proportional‐integral‐differential (PID) and active disturbance rejection control (ADRC) methods. The trial results indicate that the proposed control algorithm significantly reduces the pitch and depth errors of the AUV, resulting in a remarkable 91% success rate for dynamic docking (based on 45 tests). The lake trials demonstrate that the proposed control algorithm is highly precise and robust.

Motion control of autonomous underwater vehicle based on physics-informed offline reinforcement learning

Online reinforcement learning (RL) methods for autonomous underwater vehicles (AUV) are time-consuming and unsafe due to the need for real-world interaction. Offline RL methods can improve efficiency and safety by training with dynamic models, but an accurate model for AUV is difficult to obtain due to its highly nonlinear dynamics. These limit the application of RL methods in AUV control. To solve this issue, we propose physics-informed model-based conservative offline policy optimization (PICOPO). It offers the advantages of small dataset, strong generalizability and high safety by combining the physics-informed dynamic modelling method and the offline RL technique. First, the PICOPO constructs a physics-informed model based on a small offline dataset to serve as the digital twins (DT) of the actual AUV. This DT can forecast the long-term motion states of AUV with high-precision. The RL-based controller is then trained offline within this DT, eliminating the need for real-world interaction and allowing direct deployment to the AUV without fine-tuning. In this paper, simulations and field tests are carried out to evaluate the proposed method. Our results demonstrate that PICOPO achieves accurate motion control with just 2000 samples and enables zero-shot sim-to-real transfer, showcasing strong generalizability across various motion control tasks.

Robust H∞ Control for Autonomous Underwater Vehicle’s Time-Varying Delay Systems under Unknown Random Parameter Uncertainties and Cyber-Attacks

This paper investigates robust H∞-based control for autonomous underwater vehicle (AUV) systems under time-varying delay, model uncertainties, and cyber-attacks. Sensor and actuator cyber-attacks can cause faults in the overall AUV system. In addition, the behavior of the system can be affected by the presence of complexities, such as unknown random uncertainties that occur in system modeling. In this paper, the robustness against unpredictable random uncertainties is investigated by considering unknown but norm-bounded (UBB) random uncertainties. By constructing a proper Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) techniques, new stability criteria in the form of LMIs are derived such that the AUV system is stable. Moreover, this work is novel in addressing robust H∞ control, which considers time-varying delay, cyber-attacks, and randomly occurring uncertainties for AUV systems. Finally, the effectiveness of the proposed results is demonstrated through two examples and their computer simulations.

Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment

Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment

A lie group PMP approach for optimal stabilization and tracking control of autonomous underwater vehicles

AbstractIn this research, we explore a finite horizon optimal stabilization and tracking control scheme for the dynamical model of a 6‐DOF Autonomous Underwater Vehicle (AUV). Dynamical equations of the AUV are represented in a Lie group () framework, encompassing both translational and rotational motions. Utilizing a left Lie group action on , we define error function for velocities via a right transport map to effectively address optimal trajectory tracking. The optimal control objective is formulated as a trade‐off problem, aiming to minimize both errors and control effort simultaneously. Left action on yields the left trivialized Hamiltonian function from which the concomitant state and costate dynamical equations are derived using Pontryagin's Minimum Principle (PMP). Consequently, the resulting two‐point boundary value problem is solved to obtain optimal trajectories. We demonstrate the optimality of the resulting solution obtained from the derived control law. For ensuring boundedness in the presence of small disturbances, this study incorporates the effects of internal parametric uncertainties associated with added mass and inertia components, along with the influence of external disturbances induced by ocean currents. Through simulation validations, we confirm the alignment of our results with the theoretical developments, demonstrating that the proposed control law effectively mitigates both parametric uncertainties and ocean current disturbances.