Underactuated Autonomous Underwater Vehicles Research Articles

Electromagnetic Localization and Tracking Control of Underactuated Autonomous Underwater Vehicle for Subsea Cable Detection

Electromagnetic Localization and Tracking Control of Underactuated Autonomous Underwater Vehicle for Subsea Cable Detection

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

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

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

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

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

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.

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.

Two-player zero-sum game based neural critic tracking control for UAUV with unknown disturbance via backstepping method

In this work, a new tracking control scheme is developed for underactuated autonomous underwater vehicle(UAUV) with unknown disturbance. First, an error tracking system is constructed using backstepping method, which transforms the tracking control problem into the two-player zero-sum game problem. One player is the optimal controller which is designed to make the UAUV track the desired trajectory. The other player is the unknown disturbance that represent the overall damping effect of the UAUV. Then, the single critic network based online policy iteration algorithm is designed to get the optimal control law and the worst-case disturbance, which can achieve the near-optimal control performance and relax the requirements for initial admissible control conditions. In order to improve the converge velocity of tracking error, the weight update law are designed. Furthermore, a discount coefficient is introduced into the performance index due to the nonlinearity and the complexity of UAUV. In addition, the stability of the UAUV is analyzed based on the Lyapunov stability theory the system is guaranteed to be uniformly ultimately bounded (UUB). Finally, the effectiveness of the proposed method is demonstrated through the real-time simulations on the UAUV model.

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.

Observer based fixed-time integral sliding mode tracking control for underactuated AUVs via an event-triggered mechanism

In this paper, a fixed-time control scheme is presented for trajectory tracking of underactuated autonomous underwater vehicles (AUVs) subject to external disturbances. An adaptive fixed-time disturbance observer (AFDO) is developed to estimate the external disturbances in fixed time without the prior knowledge of external disturbances. An event-triggered mechanism is introduced to save the bandwidth resources and reduce the communication burden. By synthesizing the developed AFDO and the event-triggered mechanism, an AFDO based event-triggered fixed-time integral sliding mode control (AFDO-EFISMC) scheme is proposed to force the AUV to track the reference trajectory in fixed time. With the aid of the developed AFDO, the gain of discontinuous control part of the designed control scheme is reduced, and therefore the control chattering is alleviated. Stability analysis is given to show that the designed AFDO-EFISMC scheme can make the tracking errors of the AUV converge to arbitrarily small neighborhood of the origin within a fixed-time independent of the initial conditions. The effectiveness of the proposed control scheme is proven by comparative numerical simulations.

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

AbstractHydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under‐ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV‐MPC) is derived from the nonlinear model to enable real‐time control. In simulations, NMPC and LTV‐MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise‐time, overshoot, steady‐state error, and robustness. The LTV‐MPC shows satisfactory real‐time performance in experimental validation. The paper further also demonstrates experimentally that LTV‐MPC can be run real‐time on the AUV in performing hydrobatic maneouvers.

Path Following With Prescribed Performance for Under-Actuated Autonomous Underwater Vehicles Subjects to Unknown Actuator Dead-Zone

This paper investigates the problem of path following with prescribed performance for autonomous underwater vehicles subjects to unknown actuator dead-zone nonlinearity. To cope with this practical problem abstracted from hydrodynamic noise measurement, an adaptive command filtered backstepping method with actuator dead-zone compensation is proposed. By introducing a damped exponential barrier functions, new tracking errors are defined to satisfy the prescribed performance requirements. The path following control method is theoretically based on the command filtered backstepping technique for handling the complexity explosion problem attribute to the repeating derivations. Moreover, the filter compensation mechanism is designed to eliminate the negative effect of the filter errors. To deal with the actuator dead-zone nonlinearity, a fuzzy logic system based dead-zone compensation method is developed that dose not need the inverse of the dead-zones. Numerical simulations are conducted to demonstrate the theoretical analysis, and the usefulness and potential of the new design scheme is revealed.