Control Of Autonomous Underwater Vehicle Research Articles

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.

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.

Prescribed Performance Formation Tracking Control for Underactuated AUVs under Time-Varying Communication Delays

Achieving formation tracking control of underactuated autonomous underwater vehicles (AUVs) under communication delays presents a significant challenge. To address this challenge, a distributed prescribed performance control protocol based on a real-time state information online predictor (RSIOP) is proposed in this paper. First, we innovatively designed an RSIOP to achieve active compensation for the delayed state information of neighboring AUVs. Next, considering formation performance and safety, a low-complexity and practical nonlinear mapping function was used to implement prescribed performance tracking control for the AUV formation. Additionally, the adverse effects of external disturbance uncertainties and input saturation are also considered. Finally, the simulation tests demonstrated that the proposed formation control protocol can successfully achieve the predetermined formation tracking tasks in the presence of time-varying communication delays and external disturbances, while also enabling real-time changes in formation configuration during the process. Throughout, the protocol maintains input saturation limits, and the actual control inputs remain smooth, with no significant oscillations. Furthermore, comparative simulation tests verified the necessity of the RSIOP developed in this study and quantitatively demonstrated that the proposed control method exhibits superior performance in terms of formation control accuracy, error convergence speed, and transient-state constraints.

Neural Adaptive Intermittent Output Feedback Control for Autonomous Underwater Vehicles With Full-State Quantitative Designs.

In this article, a neural adaptive intermittent output feedback control is investigated for autonomous underwater vehicles (AUVs) with full-state quantitative designs (FSQDs). To achieve the prespecified tracking performance determined by quantitative indices (e.g., overshoot, convergence time, steady-state accuracy, and maximum deviation) at both kinematic and kinetic levels, FSQDs are designed by transforming constrained AUV model into an unconstrained model via one-sided hyperbolic cosecant boundaries and nonlinear mapping functions. An intermittent sampling-based neural estimator (ISNE) is devised to reconstruct the matched and mismatched lumped disturbances as well as immeasurable velocity states of transformed AUV model, where only system outputs after intermittent sampling are required. Using the estimations of ISNE and the system outputs after triggering, an intermittent output feedback control law incorporated with hybrid threshold event-triggered mechanism (HTETM) is designed to achieve ultimately uniformly bounded (UUB) results. Simulation results are provided and analyzed to validate the effectiveness of the studied control strategy with application to an omnidirectional intelligent navigator (ODIN).

A model-based kinematic guidance method for control of underactuated autonomous underwater vehicles

In this work, a novel guidance principle for underactuated autonomous underwater vehicles is introduced. This new method relies on the kinematic coupling between non-actuated and actuated degrees of freedom. It uses a newly introduced matrix called the Handy Matrix denoted H. The method allows reassigning unused degrees of freedom of the task to useful non-actuated DOF. The algorithm and design rules leading to the construction of H are also provided. Two different solutions based on matrix H are compared on a standard seabed scanning task.

Adaptive Transmission Interval-Based Self-Triggered Model Predictive Control for Autonomous Underwater Vehicles with Additional Disturbances

Most existing model predictive control (MPC) methods overlook the network resource limitations of autonomous underwater vehicles (AUVs), limiting their applicability in real systems. This article addresses this gap by introducing an adaptive transmission, interval-based, and self-triggered model predictive control for AUVs operating under ocean disturbances. This approach enhances system stability while reducing resource consumption by optimizing MPC update frequencies and communication resource usage. Firstly, the method evaluates the discrepancy between system states at sampling instants and their optimal predictions. This significantly reduces the conservatism in the state-tracking errors caused by ocean disturbances compared to traditional approaches. Secondly, a self-triggering mechanism was employed, limiting information exchange to specified triggering instants to conserve communication resources more effectively. Lastly, by designing a robust terminal region and optimizing parameters, the recursive feasibility of the optimization problem is ensured, thereby maintaining the stability of the closed-loop system. The simulation results illustrate the efficacy of the controller.

Enhanced three-dimensional trajectory tracking control for AUVs in variable operating conditions using FMPC-FTTSMC

In addressing the variable operating conditions encountered in complex underwater tasks, an enhanced three-dimensional tracking control method is proposed for autonomous underwater vehicle (AUV) based on fuzzy model predictive control-finite time terminal sliding mode control (FMPC-FTTSMC). Firstly, to enhance adaptability to tasks and environments, fuzzy model predictive control method is designed to achieve precise three-dimensional trajectory tracking. Additionally, a fuzzy weight allocator is employed to enable AUV to autonomously adjust optimization strategies based on real-time states such as task type, speed, and distance from the seabed, thus better coping with changing conditions and improving the universality of the control method. Secondly, a dynamic controller is designed using the finite-time terminal sliding mode control method, combined with a finite-time radial basis function neural network (FTRBFNN) disturbance observer to accurately estimate disturbances. Finally, simulation results demonstrate that the proposed method effectively reduces optimization solving time compared to traditional model predictive control, achieves trajectory tracking control under various conditions, meets preset state constraints, and demonstrates excellent tracking accuracy and robustness.

Adaptive tracking control of underactuated AUV with historical navigation information and piecewise weighted fractional order integration

In this study, a piecewise adaptive weighted fractional order integration (PAWFOI) is introduced in cascade-backstepping control, which improves the effectiveness of autonomous underwater vehicles (AUVs) in suppressing navigation errors during long-duration operations. Additionally, PAWFOI significantly reduces the computational complexity compared to conventional fractional order integration, ensuring a consistent computational speed. The adaptive weight selector dynamically adjusts the weights based on the AUV state information, achieving enhanced tracking performance. Based on Lyapunov’s theory, the results demonstrated that AUV tracking control can be achieved by selecting appropriate control parameters. Finally, an example is presented to verify the correctness and validity of the theoretical results.

Anti-disturbance cooperative formation containment control for multiple autonomous underwater vehicles with collision-free and actuator saturation constraints

This article investigates the formation containment control (FCC) problem of multiple autonomous underwater vehicles system (MAUVS) in the simultaneous presence of disturbances, collision avoidance between autonomous underwater vehicles (AUVs) and actuator saturation constraints. Firstly, for the purpose of enhancing the resisting disturbance capability of the MAUVS, the extended state observers are designed to estimate the total disturbance comprised of internal uncertainties and external interferences in real time. In order to hinder the occurrence of complexity explosion during the backstepping design, tracking differentiators are adopted to provide estimates for the derivative of the virtual control signals. Furthermore, the auxiliary system is constructed for reducing the influence of the actuator saturation on the MAUVS. Potential functions are integrated into the designed FCC strategy, so that the collisions between AUVs can be effectively avoided while the MAUVS achieving formation containment. Afterwards, via resorting to Lyapunov stability theory, it is demonstrated that the formation tracking errors and containment errors are uniformly ultimately bounded. Lastly, the comparative simulation results are illustrated to validate the feasibility of the presented anti-disturbance FCC scheme with collision-free and anti-saturation.

Approximate Optimal Adaptive Prescribed Performance Fault-Tolerant Control for Autonomous Underwater Vehicle Based on Self-Organizing Neural Networks

Approximate Optimal Adaptive Prescribed Performance Fault-Tolerant Control for Autonomous Underwater Vehicle Based on Self-Organizing Neural Networks

Adaptive meta-reinforcement learning for AUVs 3D guidance and control under unknown ocean currents

Given the challenges posed by underwater communication constraints, Autonomous Underwater Vehicles (AUVs) must operate with enhanced autonomy while balancing operational safety and efficiency within complex marine settings. AUVs face a 3D guidance and control challenge, requiring complex adjustments to unknown ocean currents that affect their stability, maneuverability, and Guidance-Navigation-Control system efficiency. This study introduces and implements an off-policy meta-reinforcement learning framework to address AUVs’ 3D guidance and control challenges under unknown ocean currents. Our method, APE-SAC, utilizes an inference network to identify latent variables, streamlining the adaptation to unknown ocean currents by optimizing their strength and direction. Consequently, this framework redefines the issue of guidance and control under unknown current disturbances into a manageable learning task across multiple predefined scenarios. Utilizing the 3D dynamics of the REMUS AUV, we construct meta-Markov Decision Processes to detail the comprehensive state space, action space, and robust reward function tailored to the task’s specific demands. Furthermore, we develop a highly realistic simulation environment specifically designed for comprehensive experiments. Extensive results demonstrate that, compared to deep reinforcement learning baselines, APE-SAC exhibits outstanding performance in guiding and controlling AUVs under unknown ocean currents.

Event-Triggered Quantitative Prescribed Performance Neural Adaptive Control for Autonomous Underwater Vehicles

Event-Triggered Quantitative Prescribed Performance Neural Adaptive Control for Autonomous Underwater Vehicles

Formation Control of Autonomous Underwater Vehicles Using an Improved Nonlinear Backstepping Method

The characteristics of autonomous underwater vehicles include nonlinearity, strong coupling, multiple inputs and multiple outputs, uncertainty, strong disturbance, underdrive, and multiple constraints. Autonomous underwater vehicle cluster systems are associated with large-scale complex dynamic systems through local perception or network communication, which have the structural characteristics of “complex dynamic + association topology + interaction rules”. To solve the problem of formation trajectory tracking of underactuated autonomous underwater vehicles, a controller was designed on the basis of an improved nonlinear backstepping algorithm, cascade system theory, and the Lyapunov direct method. In this design, the formation is determined from the actual trajectory of the leader autonomous underwater vehicle. The formation control rate is determined using the backstepping method and Lyapunov theory. Nonlinear disturbance observers were added to ensure that the trajectory error of the formation control could be quickly reduced in a real case with interference. The stability and effectiveness of this method were verified through simulation experiments. The robustness of the control algorithm was verified using two simulation cases, and the simulation results show that the proposed control method can maintain the expected formation.

An adaptive PID controller for path following of autonomous underwater vehicle based on Soft Actor–Critic

In recent years, autonomous underwater vehicles (AUVs) have witnessed rapid development, and its motion control has garnered increasing attention. Meanwhile, in industries, PID controllers are still wildly employed by most AUVs due to their simplicity, ease of deployment, and a certain level of robustness. However, they are facing significant challenges in parameter tuning, especially when dealing with various control missions and changing external environments. Deep reinforcement learning, as a data-driven approach, has gradually made its impact in AUV control. However, its lack of interpretability has hindered its deployment in relevant experiments. To address these issues, this paper proposed an adaptive PID controller for path following of AUVs based on the Soft Actor–Critic (SAC). This controller combines the interpretability of PID with the intelligence of reinforcement learning. A simulation platform was established and compared with other typical control methods, demonstrating the superiority of the proposed controller. Finally, the feasibility of the proposed SAC-PID controller was validated by lake trials. The results showed that the SAC-PID controller significantly outperformed the PID and Proximal Policy Optimization (PPO) PID controllers in multiple dimensions, such as control precision and convergence speed.

Robust optimal tracking control of multiple autonomous underwater vehicles subject to uncertain disturbances

AbstractThis paper considers the problem of robust optimal tracking control of multiple autonomous underwater Vehicles (AUVs) subject to uncertain external disturbances. First, the Takagi‐Sugeno (T‐S) fuzzy based technique is utilized to convert the high‐order nonlinear multi‐AUV system into a series of linearized subsystems. Second, a novel fully distributed sliding mode control (FDSMC) strategy is proposed to attenuate the disturbances. Meanwhile, the leader‐following consensus and the nearly optimization of the energy‐cost function for the multi‐AUV system can be achieved simultaneously through the designed optimal nominal control protocol. Moreover, the proposed control strategy has more mild constraints on the communication topologies. Finally, the effectiveness of the proposed FDSMC strategy is verified by numerical simulation studies.

Enhancing Autonomous Underwater Vehicle Decision Making through Intelligent Task Planning and Behavior Tree Optimization

The expansion of underwater scenarios and missions highlights the crucial need for autonomous underwater vehicles (AUVs) to make informed decisions. Therefore, developing an efficient decision-making framework is vital to enhance productivity in executing complex tasks within tight time constraints. This paper delves into task planning and reconstruction within the AUV control decision system to enable intelligent completion of intricate underwater tasks. Behavior trees (BTs) offer a structured approach to organizing the switching structure of a hybrid dynamical system (HDS), originally introduced in the computer game programming community. In this research, an intelligent search algorithm, MCTS-QPSO (Monte Carlo tree search and quantum particle swarm optimization), is proposed to bolster the AUV’s capacity in planning complex task decision control systems. This algorithm tackles the issue of the time-consuming manual design of control systems by effectively integrating BTs. By assessing a predefined set of subtasks and actions in tandem with the complex task scenario, a reward function is formulated for MCTS to pinpoint the optimal subtree set. The QPSO algorithm is then leveraged for subtree integration, treating it as an optimal path search problem from the root node to the leaf node. This process optimizes the search subtree, thereby enhancing the robustness and security of the control architecture. To expedite search speed and algorithm convergence, this paper recommends reducing the search space by pre-grouping conditions and states within the behavior tree. The efficacy and superiority of the proposed algorithm are validated through security and timeliness evaluations of the BT, along with comparisons with other algorithms for automatic AUV decision control behavior tree design. Ultimately, the effectiveness and superiority of the proposed algorithm are corroborated through simulations on a multi-AUV complex task platform, showcasing its practical applicability and efficiency in real-world underwater scenarios.

A Gaussian-Process-Based Model Predictive Control Approach for Trajectory Tracking and Obstacle Avoidance in Autonomous Underwater Vehicles

To achieve the efficient and precise control of autonomous underwater vehicles (AUVs) in dynamic ocean environments, this paper proposes an innovative Gaussian-Process-based Model Predictive Control (GP-MPC) method. This method combines the advantages of Gaussian process regression in modeling uncertainties in nonlinear systems, and MPC’s constraint optimization and real-time control abilities. To validate the effectiveness of the proposed GP-MPC method, its performance is first evaluated for trajectory tracking control tasks through numerical simulations based on a 6-degrees-of-freedom, fully actuated, AUV dynamics model. Subsequently, for 3D scenarios involving static and dynamic obstacles, an AUV horizontal plane decoupled motion model is constructed to verify the method’s obstacle avoidance capability. Extensive simulation studies demonstrate that the proposed GP-MPC method can effectively manage the nonlinear motion constraints faced by AUVs, significantly enhancing their intelligent obstacle avoidance performance in complex dynamic environments. By effectively handling model uncertainties and satisfying motion constraints, the GP-MPC method provides an innovative and efficient solution for the design of AUV control systems, substantially improving the control performance of AUVs.

Research on model predictive control of autonomous underwater vehicle based on physics informed neural network modeling

In the rapidly evolving field of Autonomous Underwater Vehicles (AUVs), achieving precise control remains a critical endeavor. This study presents a pioneering integration of Model Predictive Control (MPC) with a Physics-Informed Neural Network (PINN), aiming to enhance control system precision and operational efficiency in AUVs. The efficacy of MPC lies in its adept handling of the intricate constraints and inherent nonlinear dynamics intrinsic to AUV systems. Concurrently, the PINN architecture incorporates the fundamental physical laws represented by Partial Differential Equations (PDEs), augmenting the predictive fidelity of the system. Firstly, this research implements the novel PINN-enhanced MPC framework for trajectory tracking and conducts a comparative evaluation against adaptive proportional-integral-derivative (PID) and Gaussian-process-based MPC controllers. This comparative analysis elucidates the advancements in control mechanisms attributable to the PINN integration. Furthermore, this study meticulously assesses the PINN-MPC's proficiency in navigating through static and dynamic obstacles within three-dimensional marine environments, a critical capability for AUV operations. Through extensive and meticulous simulations, the proposed approach demonstrates notable progress in overcoming environmental challenges and executing intricate operational tasks, such as obstacle avoidance, with heightened efficiency and dexterity. This research constitutes a substantial contribution to the theoretical advancement and elucidation of control systems in the AUV domain, bearing profound practical implications. It lays the foundation for the development of increasingly sophisticated, advanced, and reliable AUV missions, signifying a crucial advancement in the realms of underwater exploration and operational technology.