Control Of Autonomous Underwater Vehicle Research Articles

Hybrid Layer of Improved Interfered Fluid Dynamic System and Nonlinear Model Predictive Control for Navigation and Control of Autonomous Underwater Vehicles

This study introduces a hybrid control structure called Improved Interfered Fluid Dynamic System Nonlinear Model Predictive Control (IIFDS-NMPC) for the path planning and trajectory tracking of autonomous underwater vehicles (AUVs). The system consists of two layers; the upper layer utilizes the Improved Interfered Fluid Dynamic System (IIFDS) for path planning, while the lower layer employs Nonlinear Model Predictive Control (NMPC) for trajectory tracking. Extensive simulation experiments are conducted to determine optimal parameters for both static and dynamic obstacle scenarios. Additionally, real-world testing is performed using the BlueRov2 platform, incorporating multiple dynamic and static obstacles. The proposed approach achieves real-time control at a frequency of 100 Hz and exhibits impressive path tracking accuracy, with a root mean square (RMS) of 0.02 m. This research provides a valuable framework for navigation and control in practical applications.

Adaptive Cooperative Formation Control of Autonomous Underwater Vehicles Based on Event-Triggered Mechanism

This paper investigates the coordinated motion control problem of autonomous underwater vehicles (AUVs), and an adaptive cooperative formation control method based on event-triggered mechanism is designed. In the estimation of external disturbance and model uncertainty, adaptive parameters are adopted to represent the upper bound of the learning weight matrix, which reduces the computational pressure of the system. It is proved theoretically that the signals in the system are semi-global uniformly ultimately bounded and independent of the initial state of the system. An event-triggered control mechanism is introduced, and the system updates the control law only when the event is triggered. Simulation results show that the controller can achieve the expected formation shape and save the communication resources of the system.

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

This study focuses on addressing the the coupling problem of the vertical and horizontal plane of an autonomous underwater vehicle (AUV) with an X-rudder. To guarantee the steering performance of the AUV, a depth and course control algorithm based on an L1 adaptive control algorithm (L1AC) is proposed. Firstly, the linear quadratic regulator (LQR) is designed on the basis of an AUV vertical and horizontal linear model, and the system has optimal control quality for both of the nominal vertical and horizontal models. Secondly, the nonlinear state predictor, disturbance estimator, and adaptive controller are designed separately, which is used for the compensation of the uncertainty and disturbance of the AUV dynamic. Finally, the rudder angle allocation algorithm is designed to realize the rudder angle command of the cross rudder to the X-rudder. The simulation and experiment results show that the adaptive control algorithm proposed in this paper can effectively control AUV to complete high-performance course and depth control. Based on the experiments, the prospects and shortcomings of the L1AC, LQR and MRAC algorithm in AUV control are compared and analyzed.

Parameter identification and model prediction path following control of underactuated AUV: Methodology and experimental verification

This paper explores the integrated optimal control scheme from the identification of underactuated autonomous underwater vehicle (AUV) system dynamics model parameters to the application of Model Predictive Control (MPC) technology in underactuated AUV motion control, and presents the simulation and field test results. First, the AUV kinematics and dynamics models are introduced to represent the horizontal plane motion characteristics. Based on Computational Fluid Dynamics (CFD) numerical simulation and overset mesh technology, AUV plane motion is simulated to determine the unknown hydrodynamic coefficients in the model, and the accuracy of the mathematical model is validated by comparing the maneuverability simulation and field test results. After that, an adaptive line-of-sight (ALOS) guiding algorithm with exponentially variable forward distance is utilized in the guidance layer to reconcile the rapidity and stability of tracking. The path following problem is formulated as an MPC optimization problem that takes into account multiple realistic constraints and then converted into a standard convex quadratic programming structure, permitting for online real-time optimization. Simulation and field test results indicate that the control algorithm proposed in this paper provides a more effective control strategy than the traditional LOS+PID controller in tracking effect and robustness.

Robust adaptive path following control of autonomous underwater vehicle with uncertainties and communication bandwidth limitation

This article investigates the path-following control problem for autonomous underwater vehicles (AUVs) subject to external disturbances and unknown model parameters. An event-triggered model-free adaptive control (ETMFAC) method is designed with a practical experimental platform. Firstly, a line-of-sight (LOS) guidance law is employed to obtain the desired heading angle for the AUV. Secondly, an improved model-free adaptive control method is used, in which an adaptive law is introduced to compensate for the effects caused by external disturbances. Subsequently, an event-triggering mechanism that can reduce the communication and calculation burden of the control system is proposed. As for the state mutation caused by variable disturbances, an adaptive triggering threshold is designed based on the state vector of the AUV to maintain satisfactory following performance. The stability of the control system is proved by theoretical analysis. Finally, both numerical simulations and practical experiments are conducted to evaluate the efficacy and prove the superiority of the proposed method.

ADP based output-feedback fault-tolerant tracking control for underactuated AUV with actuators faults

In this work, the output-feedback fault-tolerant tacking control issue for underactuated autonomous underwater vehicle (AUV) with actuators faults is investigated. Firstly, an output-feedback error tacking system is constructed based on the theoretical model of underactuated AUV with actuators faults. Then, an adaptive dynamic programming (ADP) based fault-tolerant control controller is developed. In our proposed control scheme, a neural-network observer is designed to approximate the system states with actuators faults. An online policy iteration algorithm is designed with critic network and action network in order to improve the tracking accuracy. Based on Lyapunov stability theorem, the stability of the error tracking system is guaranteed by the proposed controller. At last, the simulation results show that the underactuated AUV achieves better tracking performance.

Game-based distributed optimal formation tracking control of underactuated AUVs based on reinforcement learning

This paper investigates game-based distributed optimal time-varying formation tracking control problem based on backstepping technique and reinforcement learning (RL) for autonomous underwater vehicles (AUVs) with time-varying disturbances and input saturation. The proposed method enables the multi-AUV to track a desired trajectory while holding the time-varying formation. To accomplish this, we adopt a game between leader and followers to enhance robustness of the system to time-varying disturbances. The approximate optimal solution of the established Hamilton–Jacobi–Isaacs (HJI) equation is obtained by RL which performs online via the single critic Neural Network (SCNN). Hence, the optimal solution corresponds to the saddle point equilibrium of the tracking game. In addition, the command filter is used to construct the unknown hydrodynamic parameter and time-varying disturbances. Furthermore, we introduce a quasi-norm to confront input saturation. The effectiveness of the proposed approach is verified by the simulation results provided.

Self-propulsion performance predictions of AUV based on response surface methodology

The self-propulsion performance of an autonomous underwater vehicle (AUV) is predicted based on computational fluid dynamics (CFD) and response surface methodology (RSM). The innovations are that a data-driven predictive framework is proposed and a model of coupled rotation speed, self-propulsion velocity and power is developed. In the present study, the discretized propeller method and multiple reference frames (MRF) model are adopted. The design of experiment (DOE) and numerical simulations are executed with inlet velocity and propeller rotation speed as inputs and drag, thrust and torque as outputs. Using the simulation results as sample data, the prediction accuracy of multiple models is compared. The polynomial model is applied to fit the response points, describing the relationship between inputs and outputs. The goodness of fit and predictive ability is assessed using the adjusted coefficient of determination (Radj2), root mean square error (RMSE) and relative error (RE). Finally, the predicted results are further validated by experiments, and all relative errors are below 6%. A linear function of self-propulsion velocity versus rotation speed and a specific cubic function of propeller input power versus self-propulsion velocity is obtained. The prediction method and achievements are beneficial for the navigation, control and safety of AUVs.

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

In this paper, we study the quantitative design paradigm (QDP) of tracing control for a class of second-order system and further extend this to solve the trajectory tracking problem of autonomous underwater vehicles. The key merit of QDP is the capability of assigning some quantitative indices (e.g., overshoot and convergence time). To pursue performance enhancement with regard to tracking performance and bandwidth saving, a sectionalized event-triggered mechanism (SETM) incorporated with prespecified convergence time is proposed. To recognize the peculiarities of the lumped disturbances, an estimation-triggered neural network (ETNN) is designed via a property indicator, such that the disturbances that deteriorate the performance of the closed-loop system will be compensated and beneficial disturbances will be reserved otherwise, enabling less energy consumption without sacrificing the tracking performance. Theoretical analysis and simulation results are provided and analyzed, validating the performance and efficiency of the proposed solution.

Adaptive Saturated Path Following Control of Underactuated AUV With Unmodeled Dynamics and Unknown Actuator Hysteresis

This article addresses the three-dimensional (3-D) path-following problem of underactuated autonomous underwater vehicles (AUVs) in the presence of multiple uncertainties and nonlinearities, including unmodeled dynamics, actuator hysteresis, input saturation, and oceanic disturbance. First, the AUV kinetic model is reconstructed as a second-order nonlinear system by coupling the smooth saturation function and hysteresis dynamics of actuators. Second, the 3-D tracking error is derived in a simplified manner based on the relative motion theory. Then the approach angle-based symmetrical hyperbolic-tangent line-of-sight (SHLOS) guidance and kinematic control scheme are designed to ensure the global asymptotic stability of the kinematic system. Third, two sets of kinetic control schemes are designed without prior knowledge of AUV dynamics and disturbance, where the Nussbaum gain technique is applied to deal with the unknown hysteresis parameter. The application of the reconstructed kinetic model avoids the degraded performance caused by actuator nonlinearities. The first scheme is improved by adaptively estimating the bound of the generalized disturbance-like term which lumps the unmodeled dynamics, external disturbance, and saturation approximation error. The improved scheme guarantees the global stability of the system and transient tracking performance. Finally, comparative simulation studies under dynamic oceanic environments prove the effectiveness and robustness of the proposed control schemes.

Safety-critical control for autonomous underwater vehicles with unknown disturbance using function approximator

Safety and stability are two primary factors that affect the task execution performance of autonomous underwater vehicles (AUVs), especially when they operate in a disturbed environment. The unknown disturbances and unmodeled dynamics will severely degrade the performance, resulting in unsafe and unstable behaviors. This paper presents a safety-critical control framework for AUVs in the presence of unknown disturbances using a neural network function approximator. The high-order control barrier functions (CBFs) are utilized to address the safe constraints of relative degree two. A nominal controller is designed in which the unknown lumped disturbances are compensated by a radial basis function neural network, thus ensuring that the tracking errors are uniformly ultimately bounded. Besides, the function approximation and the associated bounded approximation errors are adopted to estimate the unknown lumped disturbance terms in the second-order derivative of CBFs, which alleviates the mismatch of the forward invariance condition for the safety set. The minimally invasive quadratic program (QP) is formulated to make minimal sacrifice on the performance of nominal controller. The proposed method encompasses a straightforward structure for disturbance estimation, alongside a highly efficient explicit estimating rule that promotes rapid convergence. Integrated with computationally efficient QP, the proposed method demonstrates substantial promise for real-time implementation. Comparative numerical results with disturbance observer and input-to-state safety-based robust methods reveal an improved trade-off between safety and control performance.

Research on the Influence of Turbulent Flow Induced by Dunes on AUVs

The demand for oceanic resource exploration and development is increasing, and autonomous underwater vehicles (AUVs) have emerged as potential tools for ocean exploration. To meet specific operational requirements, AUVs are required to operate in close proximity to the seafloor. However, complex and changeable seafloor terrains have complex spatiotemporal characteristics, which produce unsteady hydrodynamic interference on AUVs, affecting their safety and stability. In order to investigate the hydrodynamic characteristics of AUVs in turbulent flow fields near seafloor terrains, this study selects a typical dune terrain. Based on the computational fluid dynamics (CFD) method, the flow field around an AUV near the seafloor is simulated, and the flow field is solved using the large eddy simulation (LES) method, calculating the AUV’s hydrodynamic instantaneous and average characteristics, as well as flow field characteristics at different inflow velocities and distances from the seafloor. The results indicate that when the AUV is situated within the turbulent flow field caused by the terrain, its drag, lift, and pitch moment performance present significant fluctuations. Variations in total resistance are primarily caused by variations in pressure resistance. Variations in lift and pitch moment are more sensitive to variations in the flow field structure. The AUV also affects the development of the turbulent flow field near the terrain. When the AUV is in close proximity to the seafloor, it hampers the development of vortices below it. These results will offer guidance for the maneuverability and control of AUVs in practical engineering applications.

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

AbstractThis 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).

Adaptive event-triggered coordination control of unknown autonomous underwater vehicles under communication link faults

Coordination control of autonomous underwater vehicles finds broad applications in various marine activities. This paper focuses on the adaptive event-triggered coordination control problem of autonomous underwater vehicles under the effects of nonlinear and coupled dynamics, unknown system parameters, and communication link faults. The limited communication capacity is considered and an event-triggered distributed observer is designed under the effects of communication link faults. Reinforcement learning method is implemented to learn optimal coordination controller to achieve the position formation and the attitude synchronization for autonomous underwater vehicles with unknown dynamics. Simulation results verify the effectiveness of the proposed approach.

Research on Practical Path Tracking Control of Autonomous Underwater Vehicle Based on Constructive Dynamic Gain Controller

In this study, the stochastic nonlinear system-based trajectory tracking control problem of an autonomous underwater vehicle (AUV) is studied. We investigate the time-varying gain adaptive control method to find possible approaches to reduce the excessive computational burden. Enhanced adaptive algorithms are devised by considering the dynamic characteristics of AUV motion. By transforming the original controller design problems into parameter selection problems and subsequently solving them using the functional time-varying observer technical theorem, we can achieve optimal control performance. The control system is shown to constrain system state error due to stochastic disturbances within arbitrarily small domains. A coordinate transformation is proposed for all system states to meet boundedness conditions. We show that the closed-loop stability is confirmed, the system is asymptotically probabilistically stable, and contraction limits given in the stability analysis may be used to certify the convergence of the AUV trajectory errors. A large number of simulation studies using an underwater vehicle model have proved the effectiveness and robustness of the proposed approach. A real-time, time-varying gain constructive control strategy is further developed for the hardware-in-the-loop simulation; the effectiveness of the controller design is confirmed by introducing the controller into the AUV actuator model.

Containment Control of Autonomous Underwater Vehicles With Stochastic Environment Disturbances

This article is concerned with a containment control issue for autonomous underwater vehicles (AUVs), subject to unavailable velocity signals in cyber side and stochastic environment disturbances in physical side. We first divide the environmental disturbances into deterministic and stochastic parts. Based on this, a terminal sliding mode observer is developed to estimate the velocities of AUVs in finite time. With the estimated velocities, a distributed containment controller is designed for each AUV to follow a convex hull spanned by trajectories of the leader AUVs. For the developed velocity observer, a double power reaching law is employed to reduce the chattering and improve the convergence rate. Besides that, an adaptive strategy is incorporated into the containment controller, such that the steady-state errors caused by stochastic environment disturbances can be compensated. Stability conditions for the velocity observer and containment controller are also provided. Finally, we conduct the simulation and experimental studies to verify the effectiveness.

Cooperative Formation Control for Multiple AUVs With Intermittent Underwater Acoustic Communication in IoUT

Autonomous Underwater Vehicle (AUV) system has played an important role in complex Internet of Underwater Things (IoUT). As we know, single AUV is inefficient in collecting data in large-scale IoUT. In order to improve the timeliness of data, this paper uses multiple AUVs with specific formation shape to collect sensory data. However, the intermittent and unreliable characteristics of underwater acoustic communication between AUVs seriously affects the performance of formation control. To solve this issue, this paper mainly investigates formation control problem of leader-follower structured AUVs with state prediction estimation under the condition of unreliable underwater acoustic channel. Firstly, a novel formation control law derived from backstepping sliding mode method is proposed, and then a suitable Lyapunov functional is constructed to prove sufficient stability conditions. Secondly, Gaussian prediction model considering time delay and packet dropout has been designed to estimate the intermittent communication channel. Moreover, a new metric named formation uniform degree (FUD) is proposed to measure the degree of queue uniformity in a quantitative manner. Finally, extensive simulation results compared with traditional methods based on ideal channel model demonstrate the effectiveness of proposed formation control method.

A new path planning method for AUV based on the Navier–Stokes equations for ocean currents

The autonomous underwater vehicle (AUV), due to its flexibility, safety, and other characteristics, has been widely used in various marine applications such as underwater mapping, marine ecological monitoring and marine scientific. When navigating in complex and ever-changing ocean currents, one of the key challenges in AUV control is how to effectively avoid unknown obstacles such as sunken ships and reefs, and plan the route to reach the target point. In this paper, based on partially measured ocean current velocities, the Navier–Stokes equations (N–S equations) are used to characterize the ocean current velocities within a small-scale region. Furthermore, the improved artificial potential field method is used for path planning after integrating the velocity information. Simulation and experiments indicate that the proposed algorithm is better suited to complex marine environments.

Distributed adaptive fault tolerant formation control for multiple underwater vehicles: Free-will arbitrary time approach

This note examines the distributed free-will arbitrary time (FwAT) fault-tolerant formation control for heterogeneous multiple autonomous underwater vehicles (AUVs) operating under actuator faults, uncertainties, and exogenous disturbances. The established formation control protocol is free from initial conditions and provides higher tracking accuracy, singularity-free and smooth control signal, while the convergence time of the system is non-conservative. An adaptive nonsingular sliding mode is then developed in which the bound of lumped uncertainty is not necessary to know in advance. Due to the need for a rapid and immediate response in fixed-time control, the actuators are prone to saturation as a result of the high control torque required and to overcome this issue, an input saturation compensator is designed and incorporated into the control framework. Stability analysis under the developed control law is thoroughly performed using the Lyapunov stability theory. Finally, simulations and comparisons are demonstrated in order to verify the effectiveness of the proposed distributed formation control algorithm for a group of AUVs.

Lumped hydrodynamics identification-based cascade control for vertical-plane tracking of a fin-driven autonomous underwater vehicle

This paper aims to achieve a simplified and effective vertical-plane tracking control for a kind of fin-driven under-actuated autonomous underwater vehicles (AUVs). To this end, a two-layer framework of offline identification and online control is constructed and implemented for depth-pitch coupled tracking control of an under-actuated AUV at a constant forward speed. A simplified three-order identification model for pitch dynamics is derived and then a dedicated recursive weighted least squares algorithm is used to complete the offline estimation of lumped hydrodynamics. Subsequently, relying on identification results, an online cascade controller with just two gains and without any adaptive estimation is proposed to track the pitch guidance angle and it is proven to be input-to-state stable. Finally, comparative simulation results illustrate the effectiveness and outperformance of this two-layer framework for vertical-plane tracking control of fin-driven under-actuated AUVs.