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This paper discusses the problem of adaptive trajectory tracking control for remotely operated vehicles (ROVs). Considering thruster dynamics, a third-order state space equation is used to describe the dynamic model of ROVs. For the problem of unknown dynamics and partially known input gain, an adaptive sliding mode control design scheme based on RBF neural networks is developed using a backstepping design technique. Because of the saturation constraints of the thrusters, a first-order auxiliary state system is applied, and subsequently, a saturation factor is constructed for designing adaptive laws to ensure the stability of the adaptive trajectory tracking system when the thrusters are saturated. The proposed controller guaranteed that trajectory tracking errors are uniformly ultimately bounded (UUD). Finally, the effectiveness of the proposed controller is verified by simulations. • The thruster dynamics and saturation constraints were considered in controller design. • A third-order state space equation was constructed to describe the dynamic of ROVs. • The unknown dynamics and partially known gain were considered in controller design. • A saturation factor was constructed for the design of the adaptive laws. [ABSTRACT FROM AUTHOR]

This study focuses on the fault reconstruction for a class of second-order multi-input and multi-output (MIMO) nonlinear systems with uncertainties. An innovative design scheme of terminal sliding mode observer (TSMO) is presented for which the relative degree of the system is two. In comparison with the common sliding mode observer (SMO), the proposed TSMO can converge all state estimation errors to zero in finite time, even when some states cannot be measured directly. Given that state estimation errors converge to zero in finite time, a fault reconstruction method based on an equivalent output error injection concept and a SMO-based fault isolation strategy are presented, so that the fault information after isolating disturbances can be accurately known. Simulation examples of fault reconstruction on a small unmanned underwater vehicle are presented to demonstrate the effectiveness of the proposed method. • A novel TSMO is presented for a class of high-order uncertain nonlinear systems. • All state estimation errors converge to zero in a finite time. • A fault reconstruction strategy is developed to isolate uncertainties. • The assumptions for the uncertainties are relaxed in the proposed strategy. [ABSTRACT FROM AUTHOR]

There are significant advantages using the autonomous underwater vehicle (AUV) for underwater search. Compared with a single AUV, multi-AUV offers greater efficiency and better stability in underwater search. At the same time, the theoretical and technical level of autonomous navigation and cooperative control of multi-AUV formation is the key to the implementation of the underwater search task. The following key factors are worth discussing in the application of multi-AUV in underwater search: task allocation, path planning, and formation control. The purpose of this paper is to grasp the application and development trend of multi-AUV formation in underwater search, so as to summarize the past, present, and future research and development trends of this investigation field in detail. • To the best of authors' knowledge, this is the first systematic and comprehensive survey of multi-AUV underwater search, and is intended to assist research and development of multi-AUV underwater search applications. • Several aspects of task allocation, path planning, and formation control are considered and reviewed. The strengths, weaknesses, limitations, and challenges of the respective algorithms of the above techniques are explicitly discussed, to gain a deeper understanding of how multi-AUV multi-target search is conducted. • The opportunities and challenges of this research are listed based on the purpose of using multi-AUV underwater multi-target search. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control (MPC) method based on quantum-behaved particle swarm optimization (QPSO), combining with sliding mode control (SMC), is studied to solve the dynamic trajectory tracking problem of Unmanned Underwater Vehicle (UUV) in three-dimensional underwater environment. First, the kinematic controller based on QPSO-MPC is designed to obtain the velocity signal. QPSO limits the velocity of UUV within a specified range to handle the speed jump problem when compared with backstepping control. Then kinematic control is extended to dynamic control for verifying the feasibility of QPSO-MPC in the realistic dynamical UUV system. Here SMC is adopted in dynamic control because of its good robustness and anti-interference performance. The combination of kinematic and dynamic controllers can efficiently realize the trajectory tracking of UUV. The experimental simulation results prove that the algorithm proposed in this paper can solve the speed jump and the driven saturation caused by speed jump, and also can satisfy the propeller constraints of UUV. [ABSTRACT FROM AUTHOR]

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. • The underactuated AUVs are considered in this paper. • Graph rigidity and backstepping techniques are employed to achieve formation control. • Affine transformation technique is used to realize formation reconfiguration. • The Lyapunov based stability analysis shows that the proposed controller is stable. • Experimental results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new adaptive neural network control approach is developed for a class of remotely operated vehicles whose velocity state and angular velocity state in the body-fixed frame are unmeasured. Unlike most previous control approaches, it doesn’t need thrust model and the thruster control signal is considered as the input of control system directly. Using local recurrent neural network to approximate the unknown nonlinear functions, an adaptive observer is introduced for state estimation. Under the framework of the backstepping design, adaptive neural network control law is constructed based on the output of local recurrent neural network and state estimation. The stability analysis is given by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations. [ABSTRACT FROM AUTHOR]

A multi-AUV cooperative hunting algorithm based on bio-inspired neural network is proposed for 3-D underwater environment with obstacle. Firstly, AUV 3-D working environment is represented by biological inspired neural network model, and there is one-to-one correspondence between each neuron in neural network and the position of the grid map of underwater environment. Then the activity value of neurons is used to guide each hunting AUV navigation and obstacle avoidance. A high efficient path for each hunting AUV is arranged and finally the target is surrounded by AUVs. Lastly, to demonstrate the effectiveness of the proposed algorithm, simulation results are given in this paper. [ABSTRACT FROM AUTHOR]

In this paper, for the human occupied vehicle (HOV) system, a cascaded dynamic tracking controller for thruster fault is proposed in three-dimensional underwater workspace. Firstly, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO) for thruster failure. Secondly, the kinematics controller based on QPSO-model predictive control (MPC) is designed to obtain the speed control signal. QPSO-MPC with speed constrained optimization is proposed to solve speed jump problem caused by thruster failure. Finally, the kinematics controller is used in conjunction with dynamics controller based on the adaptive control to achieve dynamic tracking control which can solve the driving saturation problem caused by thruster failure and tracking disturbance problem by unknown ocean current disturbance. Simulation results showed the proposed dynamic tracking control with fault-tolerant control could realize stable trajectory tracking for thruster failure without the driving saturation in three-dimensional ocean current environment. • The thruster fault-tolerant control method based on QPSO is investigated for HOV. • MPC is employed to realize solution to the speed jump problem. • The speed synthesis method was combined to take the effect of the ocean current into the design of kinematics control law. • For the unknown ocean current, an observer based on adaptive-PI control was designed to estimate it. [ABSTRACT FROM AUTHOR]

Abstract: In this paper a novel hybrid control strategy is developed for trajectory tracking control of unmanned underwater vehicle (UUV). The proposed hybrid control strategy consists of two subsystems: a virtual velocity controller and a sliding-mode controller. The tracking errors are shown to asymptotically converge to zero by Lyapunov stability theory using the new approach, whereas in the traditional backstepping method, speed jump occurs if the tracking error changes suddenly. The biologically inspired model is designed to smooth the virtual velocity controller output, avoid speed jumps of underwater vehicles and satisfy the thruster control constraint. The effectiveness and efficiency of the proposed control strategy are demonstrated through simulations and comparison studies. [Copyright &y& Elsevier]

This paper considers the attitude control problem of a new class of aerial and underwater vehicles with quadrotor structures. A novel single-input FUZZY P+ID attitude controller is proposed to improve the trans-media performance of the aerial and underwater vehicle. The proposed FUZZY P+ID controller has a unique feature to deal with the nonlinear uncertainties using a set of fuzzy rules from experts. Furthermore, the single-input structure of the fuzzy control allows for fast computation with the help of the signed distance method, which avoids using lengthy look-up tables. Numerical simulations show that the proposed single-input FUZZY P+ID controller offers superior performance to the conventional PID controller. The fully operational experiments demonstrated the robustness of the proposed controller for unmeasurable disturbances, such as the buoyancy and the drag force. [ABSTRACT FROM AUTHOR]

The motion planner determines the autonomy of the autonomous underwater vehicle. However, many studies often ignore the characteristics of autonomous underwater vehicles and the underwater environment, resulting in unreasonable trajectories. To solve this problem, this paper plans the motion force through prediction and optimization to integrate the characteristics of the autonomous underwater vehicle. The fast travel algorithm is modified to take advantage of favorable currents and output arrival times, taking into account the peculiarities of the underwater environment. A novel objective function is proposed, which incorporates the arrival times to imply the requirements of obstacle avoidance and distance optimization. A penalty item is included in the objective function to avoid strong side flow. At the same time, the force and its changing rate limited in appropriate ranges are also included to balance energy consumption. In order to meet the high running time requirements in practical applications, the warm-start improved quantum particle swarm optimization is introduced into force optimization. Simulation studies are performed on a self-developed autonomous underwater vehicle. The method has good application effects in multi-island areas, ocean currents, and dynamic three-dimensional environments. Comparative simulations show that the planner plans shorter paths with good robustness and adaptability. • The planner implies obstacle avoidance and distance minimization with arrival time. • The planner conforms to the requirements of the AUV's models. • An improved QPSO is proposed for the model-predictive optimization. • The planner balances the energy consumption. [ABSTRACT FROM AUTHOR]

Underwater target detection is an important part of ocean exploration, which has important applications in military and civil fields. Since the underwater environment is complex and changeable and the sample images that can be obtained are limited, this paper proposes a method to add the adversarial occlusion network (AON) to the standard Faster R-CNN detection algorithm which called Faster R-CNN-AON network. The AON network has a competitive relationship with the Faster R-CNN detection network, which learns how to block a given target and make it difficult for the detecting network to classify the blocked target correctly. Faster R-CNN detection network and the AON network compete and learn together, and ultimately enable the detection network to obtain better robustness for underwater seafood. The joint training of Faster R-CNN and the adversarial network can effectively prevent the detection network from overfitting the generated fixed features. The experimental results in this paper show that compared with the standard Faster R-CNN network, the increase of mAP on VOC07 data set is 2.6%, and the increase of mAP on the underwater data set is 4.2%. [ABSTRACT FROM AUTHOR]

In recent years, harbour security protection has received extensive attention. For the moving invasion target interception problem in the underwater environment with ocean current, a new interception algorithm with multiple autonomous underwater vehicles (multi-AUV) called prediction planning interception (PPI) method is proposed in this paper. First, the interception position is determined quickly and simply through the movement tracking of target, and then the intercept path of each AUV in the environment is planned by the artificial potential field method. When the target is intercepted by any AUV, it is considered a successful interception. The AUV can rapidly intercept the moving target whose trajectory is dynamically changing along relatively short path in the changeable and complicated underwater environment by PPI method. The simulation results show that the interception efficiency of the prediction planning interception method is greatly improved compared with the traditional tracking interception method under different conditions. • The first time underwater interception with multi-AUVs for harbour protection. • The interception efficiency is greatly improved compared with traditional approach. • It is both applicable when the AUV velocity is equal or greater than moving target. [ABSTRACT FROM AUTHOR]

This study proposed an observer-based fault detection method for magnetic coupling underwater thrusters. To improve the accuracy of a thruster system model, a modeling identification method based on local recurrent neural networks was proposed, which can be described using state space equation. The algorithm for selecting model parameters was obtained by constructing a nonlinear constrained optimization model. Based on an identification model, a sliding mode observer was developed and employed for online fault reconstruction. Compared with traditional analytical-model-based thruster fault diagnosis methods, the proposed method can determine the fault cause to improve the submarine safety. The proposed method was validated based on the data of Jiaolong human occupied vehicle (HOV). • A modeling identification method based on local recurrent neural networks is proposed. • The parameters selecting algorithm is obtained by constructing an optimization model. • A sliding mode observer is developed and employed for online fault reconstruction. [ABSTRACT FROM AUTHOR]