Control Of Autonomous Underwater Vehicle Research Articles

Improved dynamic event-triggered anti-unwinding control for autonomous underwater vehicles

This article investigates an improved adaptive dynamic event-triggered control scheme for autonomous underwater vehicles (AUVs) under the quaternion-based attitude representation. The main innovation in this paper is to improve the dynamic event-triggered (DET) algorithm, which makes the trade-off between the control performance of the AUV system and the communication frequency in the controller-to-actuator (CA) channel easier to be achieved. Primarily, to describe the attitude representation of the AUV in a unique form, a unified auxiliary variable confining the scalar quaternion's initial error value is developed to avoid the potential unwinding phenomenon. Subsequently, based on the unified auxiliary variable, an event-based adaptive control law is tactfully designed to mitigate the adverse effects of lumped external perturbations and unknown model dynamics. Moreover, by introducing the modified trigger adjustment algorithm, the trigger frequency can be adapted to the performance requirements of the AUV system at different evolutionary stages. Finally, theoretical analysis and numerical simulations are presented to demonstrate the stability and effectiveness of the provided method.

Time-Varying Constraint-Driven Optimal Task Execution for Multiple Autonomous Underwater Vehicles

This letter focuses on the constraint-driven optimal control for multiple Autonomous Underwater Vehicles (AUVs). The different tasks are formulated as multiple constraints on optimization. First, the task goals are formulated by the time-varying control barrier functions. Then, a novel cost-to-constraint transformation method is proposed for time-varying tasks to achieve the high-order constraint-driven task execution under AUV dynamics. The priority of each task is adjusted by the maximum allowed limit of the slack variable, thus relaxing the task constraints and avoiding potential constraint conflicts. Finally, the collision-free optimal energy-aware task execution is achieved by a minimum input norm objective that is incorporated into computationally efficient quadratic programming, which is scalable to different task behaviors. Comparative numerical simulations considering time-varying trajectory tracking and cooperative coverage tasks validate the effectiveness of the proposed method.

Adaptive reinforcement learning fault-tolerant control for AUVs With thruster faults based on the integral extended state observer

In this paper, a reinforcement learning (RL) fault-tolerant control (FTC) method is proposed for trajectory tracking of autonomous underwater vehicles (AUVs) with thruster faults. To deal with the thruster fault, unknown disturbance and model uncertainty, a new integral extended state observer (IESO) for fault diagnosis observation is proposed, which uses a conventional ESO to estimate the total system uncertainty, and introduces an integral mechanism to mitigate the effect of estimation error further. Thus, the problem that the estimation error caused by the traditional ESO leads to the decline of the fault-tolerant capability of the FTC system is solved. Then, to solve the problem of integral saturation due to the introduction of the integral term, the integral term is limited after the thruster fault of the AUV. Furthermore, based on the actor–critic structure of RL, a PD-like feedback controller is designed to realize the FTC of AUV in the face of thruster fault by using the total uncertainty of the IESO scheme. And the input saturation of the thruster is considered, and an auxiliary variable system is used to handle the control truncation between saturated and unsaturated inputs. Based on the Lyapunov method, the stability of the closed-loop system is analyzed and proved. Finally, the proposed method is verified to have good fault tolerance and robustness by simulation and underwater experiments.

Markovian-Jump Reinforcement Learning for Autonomous Underwater Vehicles under Disturbances with Abrupt Changes

This paper studies the position regulation problems of an Autonomous Underwater Vehicle (AUV) subject to external disturbances that may have abrupt variations due to some events, e.g., water flow hitting nearby underwater structures. The disturbing forces may frequently exceed the actuator capacities, necessitating a constrained optimization of control inputs over a future time horizon. However, the AUV dynamics and the parameters of the disturbance models are unknown. Estimating the Markovian processes of the disturbances is challenging since it is entangled with uncertainties from AUV dynamics. As opposed to a single-Markovian description, this paper formulates the disturbed AUV as an unknown Markovian-Jump Linear System (MJLS) by augmenting the AUV state with the unknown disturbance state. Based on an observer network and an embedded solver, this paper proposes a reinforcement learning approach, Disturbance-Attenuation-net (MDA–net), for attenuating Markovian-jump disturbances and stabilizing the disturbed AUV. MDA–net is trained based on the sensitivity analysis of the optimality conditions and is able to estimate the disturbance and its transition dynamics based on observations of AUV states and control inputs online. Extensive numerical simulations of position regulation problems and preliminary experiments in a tank testbed have shown that the proposed MDA–net outperforms the existing DOB–net and a classical approach, Robust Integral of Sign of Error (RISE).

Event-triggered adaptive target tracking control for an underactuated autonomous underwater vehicle with actuator faults

In this paper, the target tracking control problem is investigated for an underactuated autonomous underwater vehicle (AUV) in the presence of actuator faults and external disturbances based on event-triggered mechanism. Firstly, the five degrees-of-freedom kinematic and dynamic models are constructed for an underactuated AUV, where the backstepping method is introduced as the major control framework. Then, radial basis function neural network (RBFNN) and adaptive control method are made full use of estimating and compensating the influences of uncertain information and actuator faults. Besides, the relative threshold event-triggered strategy is integrated into the tracking control to further reduce communication burden from the controller to the actuator. Moreover, through Lyapunov analysis, it is proved that the designed controllers guarantee that the tracking error variables of the underactuated AUV are uniformly ultimately bounded and can converge to a small neighborhood of the origin. Finally, the effectiveness and reasonableness of the designed tracking controllers are illustrated by comparative simulations.

Platoon formation control of autonomous underwater vehicles under LOS range and orientation angles constraints

A new controller design is presented on the basis of the dynamic surface control (DSC) method for the constrained platoon formation control of the underactuated autonomous underwater vehicles (AUVs) in the presence of model uncertainties with limited field-of-view (FOV) constraints. An asymmetric barrier Lyapunov function (BLF) is applied to prevent both the line-of-sight (LOS) range and orientation angles from violating limited FOV constraints. Lyapunov method is adopted to reveal that all signals of the closed-loop control system are bounded and the constrained platoon formation tracking errors are semi-globally uniformly ultimately bounded (SGUUB). An adaptive neural network technique is employed to compensate uncertain parameters and unmodeled dynamics of the vehicles. Compared with existing works in the literature, the proposed control scheme not only forces both the range and orientation angles between AUVs to their desired values in the presence of FOV constraints in the fixed-time, but also decrease the safety hazard by avoiding any possible collision between the vehicles, increase the tracking error convergence rate and improve the transient performance of the tracking controller for all AUVs. The computer simulation results demonstrate the efficiency of this newly proposed constrained platoon formation controller for the AUVs.

Fast fixed-time nonsingular terminal sliding-mode formation control for autonomous underwater vehicles based on a disturbance observer

In this paper, a novel disturbance observer-based fixed-time formation control method is proposed for autonomous underwater vehicles with actuator faults, model uncertainties and unknown disturbances. Firstly, a fast fixed-time stable system is improved, which has a faster convergence rate than existing stable system. Then, the leader-follower formation control strategy is combined with the path planning strategy based on the artificial potential field to obtain a collision-free formation configuration. Besides, a fast fixed-time disturbance observer is designed to compensate for unknown composite disturbances, which enhances the robustness of the system. Further, based on the disturbance estimation information and the improved nonsingular fast fixed-time terminal sliding-mode surface, a robust fast fixed-time formation control method is presented, which achieves the desired formation configuration. Finally, simulation and comparison results show the effectiveness of the proposed control strategy.

The multi-AUV time-varying formation reconfiguration control based on rigid-graph theory and affine transformation

This paper studies the time-varying formation reconfiguration control for multiple underactuated autonomous underwater vehicles (AUVs) with an Euler–Lagrange-like model. The goal is to control a fleet of AUVs to achieve any desired formation shape using a distance-based graph rigidity and affine transformation (GR-AT) algorithm. Firstly, the distance-based graph rigidity with backstepping technology is utilized to solve the formation controlproblem, and we acquire the initial nominal formation. In the sequel, we transform the nominal formation into any desired formation shape using the properties of the affine transformation (including translation, scaling, rotation, shearing, or a combination of them). Even the geometric shapes formed can be constantly changed. The Lyapunov stability theory can ensure the uniform ultimate boundedness of whole distance errors. Finally, several simulation examples with formation reconfiguration are given to validate the efficacy of the designed control methods. Moreover, some experimental results are presented to confirm the validity and applicability of the scheme with four real bionic robot fish.

Motion control and path optimization of intelligent AUV using fuzzy adaptive PID and improved genetic algorithm.

This study discusses the application of fuzzy adaptive PID and improved genetic algorithm (IGA) in motion control and path optimization of autonomous underwater vehicle (AUV). The fuzzy adaptive PID method is selected because it is considered to be a strongly nonlinear and coupled system. First, this study creates the basic coordinate system of the AUV, and then analyzes the spatial force from the AUV to obtain the control model of the heading angle, climb angle, and depth. Next, the knowledge of fuzzy adaptive PID and IGA technology on AVU are investigated, then fuzzy adaptive PID controllers and path optimization are established, and experimental simulations are carried out to compare and analyze the simulation results. The research results show that controllers and IGA can be used for the motion control and path optimization of AUV. The advantages of fuzzy adaptive PID control are less overload, enhanced system stability, and more suitable for motion control and path optimization of AUV.

Formation control of multiple autonomous underwater vehicles: a review

This paper presents a comprehensive overview of recent developments in formation control of multiple autonomous underwater vehicles (AUVs). Several commonly used structures and approaches for formation coordination are listed, and the advantages and deficiencies of each method are discussed. The difficulties confronted in synthesis of a practical AUVs formation system are clarified and analyzed in terms of the characteristic of AUVs, adverse underwater environments, and communication constraints. The state-of-the-art solutions available for addressing these challenges are reviewed comprehensively. Based on that, a brief discussion is made, and a list of promising future work is pointed out, which aims to be helpful for the further promotion of AUVs formation applications.

Safety-Critical Trajectory Generation and Tracking Control of Autonomous Underwater Vehicles

Safety-critical control is crucial but difficult in the applications of autonomous underwater vehicles (AUVs). This article proposes a novel hierarchical safety-critical framework for the control of AUVs consisting of waypoint-based optimal trajectory generation and tracking. The objective is to generate a smooth and feasible trajectory to conform to the dynamics of the AUV and then control the AUV to operate in a safe region. To this end, the offline trajectory generator and the online controller are designed. In the offline step, an improved minimum snap polynomial trajectory is generated as the tracking reference. Specifically, the trajectory is optimized by explicitly considering the practical constraints of the AUV's actuator, which alleviates the complexity of the controller design and computational burden compared with the real-time nonlinear optimization. In the online step, the time- and state-dependent high-order control barrier functions are incorporated into optimization through quadratic programming (QP) that modifies the backstepping-based nominal controller in a minimally invasive way. As a supplement and adjustment to the offline planning, the online step ensures that the system states are maintained in the safety set, thus ensuring obstacle avoidance. The online computational efficient QP structure guarantees convenience and scalability in practical implementation. Both the online static and dynamic obstacle avoidance simulations demonstrate the adherence to the safety constraints. Experimental results validate the effectiveness of the proposed method.

Initial-Rectification Neuroadaptive Finite-Time Surge Motion Tracking Control of Autonomous Underwater Vehicle With Input Saturation

Initial-Rectification Neuroadaptive Finite-Time Surge Motion Tracking Control of Autonomous Underwater Vehicle With Input Saturation

Autonomous Underwater Vehicle Positioning Control - a Velocity Form LPV-MPC Approach*

In this work, the positioning control of an autonomous underwater vehicle (AUV) is considered for docking operations in the presence of varying tidal currents. The AUV model is described by a dynamic model and a kinetic model, both are linear parameter varying (LPV). A velocity form LPV model predictive control (LPV-MPC) scheme is proposed, in which the AUV dynamic model is used for the states and the kinetic model is used for the output. The interdependence of AUV kinematic model and dynamic model is exploited to avoid increased state dimension. The complete velocity form controller design enables the cancellation of disturbance effects through the use of the AUV's velocity vector increment for predicting the future evolution of the system. Compared to the original predictive control for the Naminow-D AUV that uses a time-varying Kalman filter for state estimation to approximate disturbances, the proposed algorithm does not require an estimator to eliminate unknown current disturbance, therefore simplifies design and implementation. Simulation studies show the merit of the proposed controller over the original Naminow-D predictive controller especially in terms of improved transient response and reduced sensitivity to time-varying external disturbances.

Modeling and Designing Hierarchical Sliding Mode Controller for a 4-DOF Solar Autonomous Underwater Vehicles

Modeling and Designing Hierarchical Sliding Mode Controller for a 4-DOF Solar Autonomous Underwater Vehicles

Internal Model Control for AUVs with Output Time Delays and Input Disturbances

Autonomous Underwater Vehicles (AUVs) are increasingly being used for offshore inspection tasks. This paper investigates how navigation using Simple Internal Model Control for Proportional-Integral-Derivative Control (SIMC-PID) operates in a realistic offshore environment with waves and ocean current acting as input disturbances while the available underwater sensors introduce time delays on the output signals. First, the time delays are determined by investigating available absolute positioning sensor systems. Then, a model of the AUV and the external disturbances is established. The model-based SIMC-PID controller is tuned and examined based on acceptable disturbance rejection while tolerating the dominant time delays. Two simulation case studies show that the heave controller, in both cases, struggles to stabilize in 0 to 8 meters depths, while the surge and sway controllers tolerate the Doppler Velocity Log case (DVL) acceptably. Short Baseline (SBL) shows unacceptable performance in 0 to 15 meters depths. It is concluded that the simplicity of the SIMC-PID controller is an advantage and, therefore, useful when time delays are relatively small, but more advanced techniques must be applied for larger delays such as those introduced by SBL systems.

Guidance, Navigation, and Control of AUVs for Permanent Underwater Optical Networks

Ocean exploration is in its infancy with more than 80% of the oceans still unexplored. Contrary to space exploration, electromagnetic waves have seldom been used underwater because of their heavy absorption by seawater. In this article, we present guidance, navigation, and control systems to steer autonomous underwater vehicles (AUVs) such that a permanent optical network can be set up between the deep sea and the surface. The challenge is to guarantee that there is consistently a path through the AUV network to relay the signal for an unlimited time despite the finite battery's lifetime of the AUVs. To achieve this, the guidance system combines a decentralized model predictive control (D-MPC) with graph theory to compute the trajectory of each agent relaying the optical signal as well as of redundant agents forming local leader–follower formations with each relay acting as leader of its own subfleet. The D-MPC solves a constrained optimization problem where the communication and collision avoidance constraints are formulated to enable two successive leaders and one of their followers to remain within optical range and thus allowing to immediately re-route the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> network if a leader fails to relay the signal. Its combination with graph theory allows to change the dynamic topology of the swarm, with additional agents diving from a charging station, such that the network can relay the optical signal for an unlimited time.

Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

Most formation approaches of autonomous underwater vehicles (AUVs) focus on the control techniques, ignoring the influence of underwater channel. This paper is concerned with a communication-aware formation issue for AUVs, subject to model uncertainty and fading channel. An integral reinforcement learning (IRL) based estimator is designed to calculate the probabilistic channel parameters, wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design (M-PCM-OFFD) is employed to evaluate the uncertain channel measurements. With the estimated signal-to-noise ratio (SNR), we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs, dealing with uncertain dynamics and current parameters. For the proposed formation approach, an integrated optimization solution is presented to make a balance between formation stability and communication efficiency. Main innovations lie in three aspects: 1) Construct an integrated communication and control optimization framework; 2) Design an IRL-based channel prediction estimator; 3) Develop an IRL-based formation controller with M-PCM-OFFD. Finally, simulation results show that the formation approach can avoid local optimum estimation, improve the channel efficiency, and relax the dependence of AUV model parameters.

A Novel 3D Path Following Scheme for a Class of Underactuated Underwater Vehicles with Unconventional Actuation

A 3D path following algorithm involving a novel motion control methodology is investigated for use on a new class of autonomous underwater vehicle (AUV). Unlike standard path following AUV control algorithms for underactuated vehicles, where roll perturbations are avoided, this unconventional algorithm utilizes active roll control to achieve a desired heading. The desired heading is calculated using traditional look-ahead techniques, and is achieved by performing a roll rotation to align the body-fixed yaw plane with the desired heading, and then a yaw rotation is performed to achieve that desired heading. It is demonstrated that this new path following algorithm achieves good tracking results, but experiences complications for increased forward velocities. It is also demonstrated that the complications at higher velocities can be overcome with adaptive adjustment of the look-ahead algorithm parameters.

Finite-time self-structuring neural network trajectory tracking control of underactuated autonomous underwater vehicles

A finite-time self-structuring neural network (SSNN) trajectory tracking control scheme is proposed for an input-constrained underactuated autonomous underwater vehicle (AUV) with unknown external disturbances. First, a time-varying barrier Lyapunov function is proposed to constrain and prevent excessive consistency errors and reduce computational effort. Second, a finite-time controller is presented to achieve finite-time convergence of the closed-loop system, while dynamic surface control (DSC) is used to reduce the computational complexity of the system. Third, a finite-time SSNN method is developed to obtain the optimal number of neurons with simple computation and better approximation to estimate uncertain disturbances. Moreover, the Lyapunov stability proof indicates that all signals in the closed-loop system are ultimately bounded uniformly with respect to the initial constraints, and the trajectory tracking errors can converge to near zero in a finite time. Finally, simulations not only evaluate the performance of the proposed controller-controlled system but also verify the effectiveness of the methodology in this paper.

Path-following optimal control of autonomous underwater vehicle based on deep reinforcement learning

In this paper, optimal control is applied in the path-following control problem for the autonomous underwater vehicle (AUV) when the geometry information of path is known and will not change over time. Aiming at the problem that the optimization time is too long in one control step for complex nonlinear problems, a path-following control method based on Simplified Deep Deterministic Policy Gradient (S-DDPG) algorithm is proposed. In S-DDPG, only the reward in the current state is considered, and the future reward does not need to be predicted, which avoids generating amount of meaningless failed samples and simplifies the training process of neural networks (NNs). The training is performed and completed offline before the beginning of path-following where the NNs are directly used as the controller. The simulation results show that the S-DDPG can make the AUV complete path-following tasks and has obvious advantages compared with other methods.