Underactuated Autonomous Underwater Vehicles Research Articles

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.

Research on obstacle avoidance of multi-AUV cluster formation based on virtual structure and artificial potential field method★

The formation of autonomous underwater vehicle (AUV) in the three-dimensional environment is very important for carrying out complex ocean tasks. During formation sailing, it is necessary to consider avoiding obstacles while maintaining the formation. In this paper, an AUV formation obstacle avoidance method based on virtual structure and artificial potential field method is proposed. Firstly, a complete mathematical modeling of AUV is carried out, and a sliding mode position tracker based on virtual position points is designed based on this model. Then the tracking points of each AUV in the formation are calculated according to the expected trajectory and formation feature structure to ensure that the spacing between AUVs meets the requirements of the formation. Finally, the artificial potential field method is introduced into the position tracker to avoid obstacles between the obstacle and the AUV. Five groups of simulation experiments of parallel formation, vertical formation, cross formation, triangular formation and unknown complex environment are designed. The formation scalability of the algorithm is simulated and compared with the virtual leader method in related references. Finally, KKSwarm platform is used to conduct a real simulation in 2D environment to verify the effectiveness of the proposed algorithm.The experimental results show that the proposed method can effectively realize the formation navigation of multiple underwater vehicles and successfully avoid obstacles, which provides an effective method to solve the problem of the formation obstacle avoidance of underactuated AUV.

Segmented hybrid event‐triggered control for underactuated autonomous underwater vehicles with an asymmetrical prescribed performance constraint

AbstractIn this technical article, the trajectory‐tracking problem of event‐based adaptive prescribed performance control for underactuated autonomous underwater vehicles (AUVs) is considered. The primary innovation of this article is the proposal of a segmented event‐triggered mechanism (SETM) that incorporates preselected convergence time. This mechanism allows for the regulation of communication frequency in the control channel (controller‐to‐actuator) according to the dynamic characteristics of the AUV system at different stages. In addition to presenting new design scheme based on SETM, an enhanced vision known as segmented hybrid event‐triggered mechanism (SHETM) is introduced. Notably, the minimum and maximum triggering intervals (MITI and MATI) for these two ETMs can be calculated from the corresponding resettable dynamic variables presented in the trigger conditions. Subsequently, by utilizing the proposed asymmetrical prescribed performance control (APPC) strategy, the system's output tracking error behaviors in both transient and steady‐state stages can be qualitatively predetermined through design parameters within the boundary function. Moreover, the minimum learning parameter (MLP) based RBFNN adaptive control law is developed to counteract the effects of model uncertainties and ocean current perturbations. Finally, rigorous theoretical analysis and simulation results confirm the viability of the proposed scheme.

AUV way-point tracking at constant velocity: cross-track error (CTE) and line-of-sight (LOS) guidance

This paper addresses the integrated challenges of path-following and tracking control for an under-actuated Autonomous Underwater Vehicle (AUV) in the two-dimensional (2D) plane. Four distinct 2D trajectories: linear, linear with sharp turns, curved, and circular trajectories have been considered in this study. The proposed path-following control algorithm leverages AUV kinematic and dynamic models, incorporating the Cross-Track Error (CTE) method and Line-of-Sight (LOS) technique to determine the desired orientation. Stability analysis has been performed to evaluate the robustness of the controllers against sudden underwater disturbances. Additionally, perturbation has been introduced in simulations to mimic real-world conditions more accurately. The simulations confirm the controllers’ proficiency in accurately tracking these various trajectories from a given starting point.

Backstepping-based adaptive control of underactuated AUV subject to unknown dynamics and zero tracking errors

This paper introduces a zero-error tracking control solution for underactuated autonomous underwater vehicle (AUV) facing unknown dynamic and environmental disturbances. The proposed approach integrates the backstepping method with adaptive techniques and neural networks to effectively address the challenges posed by uncertainties and external disturbances, thereby enhancing the robustness and adaptability of AUV. Subsequently, a boundary estimation method is employed to dynamically update a single adaptive parameter online, leading to a significant reduction in computational workload. To achieve superior asymptotic tracking performance, a different coordinate transformation of fusion scalar function is designed. Moreover, a rigorous analysis of the proposed control scheme is conducted using the Lyapunov-based method, establishing that the tracking error converges asymptotically to zero. Simulation experiments are executed to illustrate the efficacy of the proposed control scheme.

Double-loop LQR depth tracking control of underactuated AUV: Methodology and comparative experiments

In this paper, a double-loop depth tracking control scheme based on linear quadratic regulator (LQR) is proposed for underactuated autonomous underwater vehicles (AUV). The motivation is to address the problem that the classic single-loop LQR depth controller which is easy to use in practice yet cannot effectively overcome the underactuated AUV features and complex disturbances. First, a double-loop depth control framework is proposed which divides complex disturbances into outer-loop kinematic disturbances and inner-loop dynamic disturbances. Second, the adaptive line-of-sight (ALOS) guidance law is designed in the outer loop to account for the underactuated characteristics. The depth tracking error is converted to the desired pitch and the kinematic disturbance caused by the unknown angle of attack is compensated. Furthermore, based on the linear model in the inner-loop, a concise LQR method is designed to track the desired pitch. Meanwhile, an extended state observer (ESO) is introduced to estimate dynamic disturbances such as model nonlinearity, uncertainty and external environmental disturbances. Finally, the proposed scheme is validated by tank test and compared with the single-loop LQR method. The experiment results show that the proposed double-loop depth tracking method can effectively overcome complex disturbances and the depth tracking accuracy is increased by 83%.

Three‐dimensional path following control of underactuated autonomous underwater vehicles with nonzero roll dynamics: A novel line‐of‐sight‐guided approach

AbstractMost existing path following control approaches for underactuated Autonomous underwater vehicles (AUVs) assumed that the roll angle can be passively stabilized at zero, and simplified the problem to the 5‐degree‐of‐freedom model. However, in practice, the roll motion suffers from undesirable oscillations induced by environmental disturbances, unbalanced propeller torque, etc. To preserve the stability of the system under nonzero roll dynamics, this paper proposes a novel line‐of‐sight‐ (LOS) guided path following control approach. Firstly, the spatial path following error dynamics model is derived to comprehensively reveal the negative effect of roll dynamics. Secondly, an improved LOS guidance law is designed to compensate for the nonzero roll angle. A salient feature is that the proposed guidance law can be applied to the underwater vehicle no matter it is underactuated in the roll motion or not. Thirdly, by assigning the guidance angles to the desired attitude angles, a robust attitude tracking controller is developed, in which the adaptive estimation technique is employed to handle the lumped uncertainties. The tanh‐based saturation function is incorporated such that the resultant control signals are inherently bounded. The closed‐loop path following and attitude tracking errors are proved to be globally asymptotically stable at the origin. Comparative simulation results are provided to substantiate the effectiveness and superiority of the proposed method.

Iterative learning–based model-free adaptive precise heading following of an autonomous underwater vehicle with unknown disturbances

Due to the nonlinearity, strong coupling, and uncertain parameters of autonomous underwater vehicle (AUV), it is difficult to build an accurate dynamic model, which makes precise control of AUV extremely challenging. To handle the precise heading-following problem of AUV, this paper proposes an iterative learning-based redefine model-free adaptive heading control (IL-RMFAC) method for the underactuated AUV with unknown disturbances based on data driven. The control scheme consists of a learning control algorithm, a parameter iterative update algorithm, and a parameter reset algorithm. It is designed using only the input and output (I/O) data of the controlled system and is a data-driven control method. The pseudo partial derivative (PPD) can be iteratively calculated through the parameter iterative update algorithm and reset algorithm to adjust the learning gain, solving the problem of strictly limited initial position of the traditional fixed learning gain iterative learning control (ILC). A linear combination of angle and angular velocity is introduced in the kinematic layer to avoid overshooting of the expected following target, and an iterative learning method is introduced in the dynamics to improve the accuracy. As the number of iterations increases, the steady-state error is gradually decreased. Finally, by comparing traditional proportional–integral–derivative (PID) simulations, the proposed algorithm’s effectiveness and outstanding performance for the AUV heading tracking are confirmed.

Backstepping method tracking control for underactuated AUV with unknown dynamics based on action-critic networks based ADP

This work investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Due to the unknown dynamics, an action-critic networks based adaptive dynamic programming (ADP) scheme combined with backstepping approach is designed, which can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the proposed control scheme.

High-Speed Obstacle Avoidance of a Large-Scale Underactuated Autonomous Underwater Vehicle Under a Finite Field of View

High-Speed Obstacle Avoidance of a Large-Scale Underactuated Autonomous Underwater Vehicle Under a Finite Field of View

Self-Adjusting Prescribed Performance H∞ Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles

Self-Adjusting Prescribed Performance H∞ Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles

Intelligent Trajectory Tracking and Formation Control of Underactuated Autonomous Underwater Vehicles: A Critical Review

Intelligent Trajectory Tracking and Formation Control of Underactuated Autonomous Underwater Vehicles: A Critical Review

Adversarial deep reinforcement learning based robust depth tracking control for underactuated autonomous underwater vehicle

In this paper, an adversarial deep reinforcement learning-based control method is proposed to address the issue of robust depth tracking of an underactuated autonomous underwater vehicle in the presence of intrinsic coupled dynamics and external disturbances. First, long-short-term-memory neural network is presented to memorize and predict the changes in the state of vehicle, and a cascaded multilayer perception projects the output into action space of vehicle. Subsequently, adversarial deep reinforcement learning scheme is applied to the training of control agent by introducing an adversary which counteracts the control behavior, whereby the agent is enabled to learn the control strategy in different distributions of state transition. For evaluation of the performance, a control agent is pre-trained in simulation environment based on the reliable digital model of a real vehicle, and the simulation environment is paced at one iteration per second to align with real-time operations to ensure the portability of training result. Furthermore, the training cost is also extremely reduced. Finally, experiments are conducted with time-varying disturbances to further prove the feasibility of the proposed learning-based control scheme on a prototype of underwater vehicle in towing tank. Moreover, comparative experiment results show the better robustness performance of the learning-based control agent than that of classic line-of-sight based proportional–integral–derivative and adaptive line-of-sight based proportional–integral–derivative controllers in different scenarios.

Average consensus time-varying formation control of underactuated autonomous underwater vehicles based on event triggering decoupling mechanism

This paper focuses on the challenge of time-varying formation control for underactuated autonomous underwater vehicles (AUVs). To tackle this challenge, an average consensus time-varying formation control based on event triggering decoupling mechanism (ETDM-ACTVF) is proposed. Firstly, the average consensus time-varying formation (ACTVF) based on the affine transformation are introduced, and the control objective considering the dynamic characteristics of underactuated AUVs is proposed. The trajectory tracking controller is designed by the back stepping method. Secondly, an event triggering decoupling mechanism (ETDM) achieves the state decoupling to constrain the uncontrollable state. Moreover, a dynamic event triggering mechanism is employed to reduce the execution frequency of actuators. Finally, the excellent performance of the proposed algorithm in addressing both the underactuated AUV time-varying formation control and decoupling control problems is demonstrated through simulation results.

Robust Path-Following Control for AUV under Multiple Uncertainties and Input Saturation

In this paper, a robust path-following control strategy is proposed to deal with the path-following problem of the underactuated autonomous underwater vehicle (AUV) with multiple uncertainties and input saturation, and the effectiveness of the proposed control strategy is verified by semi-physical simulation experiments. Firstly, the control laws are constructed based on the traditional backstepping method; the multiple uncertainties are treated as lumped uncertainties, which can be estimated and eliminated by the employed extended state observers (ESOs). In addition, the influence of input saturation can be compensated by the designed auxiliary dynamic compensators. Secondly, to simplify controller design and address the “complexity explosion”, two command filters are used to obtain the estimated value of the unknown sideslip angular velocity and the desired yaw angular acceleration, respectively. Finally, the superiority and robustness of the proposed control strategy are verified through computer simulation. A semi-physical simulation experiment platform is built based on the NI Compact cRIO-9068 and PLC S7-1200 to further demonstrate the effectiveness of the proposed control strategy.

Adaptive nonsingular fast terminal sliding mode control for the tracking control of underactuated autonomous underwater vehicles

This article proposes an adaptive nonsingular fast terminal sliding mode control scheme with piecewise fast multi-power reaching law for tracking control of underactuated autonomous underwater vehicles under model uncertainties, ocean disturbances, and measurement noise. This control approach enhances the robustness and guarantees faster convergence of state error to zero in finite time while reducing the chattering effect. Utilizing the benefit of adaption law prevents overestimating control parameters, and it eliminates the need for the upper bound value of disturbances. The overall stability of the system is analyzed using the Lyapunov criterion. The results of the proposed approach are compared with adaptive nonsingular terminal sliding mode control and adaptive sliding mode control. The performance of the proposed control approach is evaluated by using the performance indices root mean square error and chattering indicator. The simulation results confirm the efficiency of the proposed approach.

Disturbance suppression and NN compensation based trajectory tracking of underactuated AUV

For the trajectory tracking of underactuated autonomous underwater vehicle (AUV) subject to disturbances, a disturbance suppression and neural network (NN) compensation based control strategy is proposed. Lyapunov method is used to guide the overall design of control system. To deal with the underactuation, Light of sight (LOS) guidance is used to establish the relationship between heading angle and cross-track error. To suppress the external disturbance, L2-gain design is employed to guarantee the controller robustness. To improve the tracking accuracy, on-line neural networks with guaranteed stability are designed to identify unknown dynamics including the derivatives of virtual controls and errors induced by input saturation. Numerical simulation is performed to verify the effectiveness of the proposed control strategy. Compared with sliding mode controller (SMC) and disturbance observer approaches, the proposed controller performs better in terms of robustness and the input saturation is alleviated.

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.

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.