Control Of Autonomous Underwater Vehicle Research Articles

Homing tracking control of autonomous underwater vehicle based on adaptive integral event-triggered nonlinear model predictive control

The homing problem of autonomous underwater vehicle with constraints and bounded disturbances is studied in this paper. More specifically, a nonlinear model predictive control (NMPC) framework based on adaptive integral event-triggered (AIET) mechanism is designed for AUV tracking mobile docking station (DS). Firstly, according to the characteristics of AUV homing, a guidance system based on mobile DS is designed, which can provide a feasible reference trajectory for the subsequent AUV tracking independent of the vehicle model. Then, in the framework of AIET-NMPC control, the designed tracking controller can reduce the computational cost while guaranteeing the control accuracy. The proposed AIET mechanism can dynamically adjust the trigger threshold according to the integral error between the real state and the optimal state of the system. In addition, by designing exponential robust constraints in the NMPC optimization problem, the system stability margin is increased. Finally, it strictly proves the Zeno free behavior, the feasibility of the algorithm, and the stability of the system. Simulation results demonstrate that the proposed control approach yields better effects in terms of tracking control performance and computational performance compared to other methods.

Adaptive Predefined-Time Optimal Tracking Control for Underactuated Autonomous Underwater Vehicles

Dear Editor, This letter concentrated on the adaptive predefined-time optimal tracking control for underactuated autonomous underwater vehicles (AUVs). According to adaptive dynamic programming (ADP) with actor-critic neural networks (NNs), by constructing a novel barrier-type cost function, an adaptive predefined-time optimal constraint control approach was developed. With the help of predefined-time stability theory via tanh(·) function, the developed control approach could ensure all state errors did not beyond the preset error boundary, and the controlled system was semi-global practical predefined-time stable. At the same time, the minimum of cost function could be guaranteed. Finally, simulation results were given to verify the effectiveness of the developed control approach.

Adaptive asymptotic tracking control for autonomous underwater vehicles with non-vanishing uncertainties and input saturation

This article provides an adaptive asymptotic backstepping-based tracking control algorithm for autonomous underwater vehicles (AUVs) under the situation of non-vanishing uncertainties and input saturation. Firstly, the adaptive asymptotic control algorithm is developed to compensate the non-vanishing uncertainties. And then, virtual control stabilizing functions are reconstructed to improve the system performance by virtue of variable substitutions. Next, the hyperbolic tangent function is applied to handle the unknown input saturation problem. Based on the Lyapunov stability theory, the asymptotic stability of AUVs is proven. Finally, simulation results illustrate that the control strategy can effectively inhibit the effect of unknown model dynamics on the system.

A neural network based efficient leader–follower formation control approach for multiple autonomous underwater vehicles

This manuscript proposes an efficient and novel method for the leader–follower formation control of multiple autonomous underwater vehicles. The goal is to efficiently allow the follower to follow the desired formation that the leader has presented. It is noteworthy how the model-dependent and model-free control schemes are combined to achieve this goal. The dynamical model of the system always contains inherent uncertainties, so the model-based control technique is incapable to manage these systems. Due to uncertainties and external disturbances, it is impossible to have complete knowledge of the system’s dynamic model in real-world applications. Therefore, whatever the partial information about the dynamic model is available has been used in the design of the controller. The approximation ability of the neural network is used to enhance the inefficiency of the model-based control strategy. The unknown dynamics of the system is estimated via a radial basis function neural network. An adaptive compensator is introduced to the controller’s part to counteract the effects of neural network reconstruction errors as well as those of external disturbances. After that, a Lyapunov function makes use of the online adaptive laws for the parameter vector as well as for the weights of neural network which ensures that the system is stable. As a result, by Barbalat’s lemma, formation errors not only stay inside the desired levels but also converge to a small neighbourhood of zero asymptotically. At long last, contrastive MATLAB simulations are conducted to affirm the capability and superiority of the suggested technique.

An Improved S-Plane Controller for High-Speed Multi-Purpose AUVs with Situational Static Loads

The classic S-plane control method combines PD structure with fuzzy control theory, with the advantages of a simple control structure and fewer parameters to be adjusted. It has been proved as a practical method in an autonomous underwater vehicle (AUV) motion control at low and medium speeds, but it takes no account of the situational static load and varying hydrodynamic forces which influence the control quality and even result in a “dolphin effect” at the time of high-speed movement. For this reason, an improved S-plane controller is designed based on the sliding mode variable structure, sliding mode surface, and control items in order to respond to the situational static load and high-speed movement. The improved S-plane controller is verified by Lyapunov stability analysis. The thrust allocation strategies are also discussed with constraints introduced in accordance with task requirements. In order to verify the practicability and effectiveness of the improved S-plane controller, both simulation experiments and field trials of AUV motion control, long-range cruise, and path point following were carried out. The results have demonstrated the superiority of the improved S-plane controller over the classic S-plane controller.

Adaptive fixed-time backstepping control for three-dimensional trajectory tracking of underactuated autonomous underwater vehicles

An adaptive fixed-time backstepping control is proposed to achieve the three-dimensional trajectory tracking control of an underactuated autonomous underwater vehicle (AUV) in the presence of model uncertainty and external disturbances. In this paper, the dynamics of the AUV in terms of five degrees of freedom (DOFs) are discussed. Considering it is an underactuated AUV, a virtual velocity guidance law is derived using the backstepping method. For velocity convergence, an adaptive fixed-time control is derived without model parameters, with adaptive adjusting law tackling system unknows. Theoretical analyses demonstrate that the tracking error converges to a small bounded field within a fixed time in the proposed control scheme. The effectiveness and superiority of the proposed method are verified by simulation results.

Reference Model-Based Deterministic Policy for Pitch and Depth Control of Autonomous Underwater Vehicle

The Deep Reinforcement Learning (DRL) algorithm is an optimal control method with generalization capacity for complex nonlinear coupled systems. However, the DRL agent maintains control command saturation and response overshoot to achieve the fastest response. In this study, a reference model-based DRL control strategy termed Model-Reference Twin Delayed Deep Deterministic (MR-TD3) was proposed for controlling the pitch attitude and depth of an autonomous underwater vehicle (AUV) system. First, a reference model based on an actual AUV system was introduced to an actor–critic structure, where the input of the model was the reference target, the outputs were the smoothed reference targets, and the reference model parameters can adjust the response time and the smoothness. The input commands were limited to the saturation range. Then, the model state, the real state and the reference target were mapped to the control command through the Twin Delayed Deep Deterministic (TD3) agent for training. Finally, the trained neural network was applied to the AUV system environment for pitch and depth experiments. The results demonstrated that the controller can eliminate the response overshoot and control command saturation while improving the robustness, and the method also can extend to other control platforms such as autonomous guided vehicle or unmanned aerial vehicle.

Dynamic Target Tracking Control of Autonomous Underwater Vehicle Based on Trajectory Prediction.

Underwater dynamic target tracking technology has a wide application prospect in marine resource exploration, underwater engineering operations, naval battlefield monitoring, and underwater precision guidance. Aiming at the underwater dynamic target tracking problem, an autonomous underwater vehicle tracking control method based on trajectory prediction is studied. First, a deep learning-based target detection algorithm is developed. For the image collected by the multibeam forward-looking sonar image, this algorithm uses the YOLO v3 network to determine the target in a sonar image and obtain the position of the target. Then, a time profit Elman neural network (TPENN) is constructed to predict the trajectory information of the dynamic target. Compared with an ordinary Elman neural network, its accuracy of dynamic target prediction is increased. Finally, underwater tracking of the dynamic target is realized using the model predictive controller (MPC), and the tracking result is stable and reliable. Through simulations and experiment, the proposed underwater dynamic target tracking control method is demonstrated to be effective and feasible.

Prescribed time observer based trajectory tracking control of autonomous underwater vehicle with tracking error constraints

This paper addresses the trajectory tracking control problem of the autonomous underwater vehicle with tracking error constraints and prescribed time convergence. In view of the fact that underwater vehicle is inevitably influenced by unknown external disturbances and input saturation, a disturbance observer is first proposed to achieve prescribed time disturbance attenuation. By virtue of the proposed observer, the observation error will arrive and maintain at a user-defined compact set in a prescribed time, which lays the foundation for subsequent control design. Then, an auxiliary dynamic system with time varying gain is constructed to deal with the input saturation phenomenon. Finally, a robust tracking control scheme is derived by integrating the proposed observer and auxiliary dynamic system into the prescribed performance control architecture, which results in both prescribed time convergence and transient performance constraints in the presence of input saturation and disturbances. Numerical simulation studies are carried out to illustrate the superiority and benefits of the proposed algorithm.

Adaptive distributed observer-based predictive formation tracking control for autonomous underwater vehicles

Numerous existing autonomous underwater vehicles (AUVs) formation control works have focused on the observation of external time-varying disturbances in trajectory tracking control. However, external time-varying disturbance outliers cannot be ignored due to the complex marine environment. It is rarely considered in previous research. In this paper, a double-layer distributed formation prediction control scheme is designed, including an upper-level adaptive distributed observer and a lower-level predictive controller. Firstly, the velocity is observed and regarded as an unknown variable by the expectation maximization algorithm. The gain in the observer will be optimized by adaptive cubature Kalman to improve the anti-disturbance performance of the adaptive distributed observer. Then, the constraints of internal safety distance and input saturation are fully considered. The distributed cooperative cost function is constructed based on the virtual trajectory, which is further introduced into local rolling domain optimization to realize formation predictive control. The boundedness of innovation and Lyapunov stability are utilized to prove the reliability of the control system. Some comparative simulation experiments are designed to demonstrate the effectiveness and robustness of the proposed method.

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.