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In this paper, for the remotely operated vehicle (ROV) system, a cascaded dynamics trajectory tracking controller for thruster fault is proposed. Firstly, the control strategies are used to reallocate the thruster forces based on quantum-behaved particle swarm optimization (QPSO). Secondly, the kinematic 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 kinematic controller method is used in conjunction with dynamic controller based on the sliding mode control (SMC) to achieve dynamic tracking control, and to solve the driving saturation problem caused by thruster failure. Experimental results showed the proposed dynamics trajectory tracking controller could realize trajectory tracking problem under thruster failure without the driving saturation. [ABSTRACT FROM AUTHOR]

Underwater image processing has always been a very challenging problem. Under the influence of environmental factors, underwater images are prone to some problems, such as colour cast, low visibility, and few edge details. Here, an image enhancement algorithm is proposed to improve image degradation mainly caused by the absorption of light. First, colour compensation and white balance algorithm are used to restore the natural appearance of the image. Then the improved dark channel prior (DCP) is used to improve the visibility and avoid blocking artifacts which appear in traditional DCP. Unsharp masking (USM) is applied to enhance the texture features of the DCP image. Finally, wavelet fusion is used to fuse the DCP image and DCP+USM image. The fusion algorithm not only further improves the visibility and texture features, but also reduces the noise of DCP+USM. Compared with other methods, quantitative analysis results show that the enhanced images have higher visibility, more details and edge information. [ABSTRACT FROM AUTHOR]

To ensure that the unmanned underwater vehicles (UUVs) can still successfully complete the corresponding tasks in the case of thrusters having faults, a novel fault-tolerant control strategy which combines the model predictive control (MPC) algorithm and the fault-tolerant reconstruction algorithm is presented in this paper. Firstly, a cascade control method based on model predictive control algorithm is introduced, and then the problem when the thrusters in the faulty condition are discussed. For different degrees of thruster fault, the method of weighted pseudo inverse and quantum particle swarm optimization (QPSO) is used for hybrid fault-tolerant control (FTC). At the same time, to overcome the limitations brought by the pseudo inverse reconstruction algorithm, an optimization criterion with the infinite norm as the cost function is introduced into the QPSO algorithm to accelerate the search for the optimal solution in the feasibility space so as to ensure the feasibility of the solution. The simulation results show that the fault-tolerant control method proposed based on MPC (FTC-MPC) in this paper can provide ideal control effects for the unmanned underwater vehicles. [ABSTRACT FROM AUTHOR]

Path planning is a prerequisite for autonomous underwater vehicles to perform tasks autonomously. Many shortest distance algorithms are applied, and ocean currents are ignored to plan a short path in distance, which is usually time and energy consuming. In fact, the favourable currents can be exploited while avoiding the opposite ocean flows. Based on the bioinspired neural network architecture, this paper proposes a novel dynamic neural network model to plan the time-saving path in ocean current environments. After that, the path is smoothed by the B-spline algorithm. Analysis of the model shows that it can find out the minimum time path. Many simulations have also been introduced to test the effectiveness of the proposed model, showing good results. The dynamic neural network model has no learning procedure and can run in parallel. It has the advantages of loose parameter restrictions and wide spreading of neural activities. In addition, it has also been proven to be suitable for strong ocean currents. [ABSTRACT FROM AUTHOR]

With the development of sensor technology, sensor nodes are increasingly being used in underwater environments. The strategy presented in this paper is designed to solve the problem of using a limited number of autonomous underwater vehicles (AUVs) to complete tasks such as data collection from sensor nodes when the number of AUVs is less than the number of target sensors. A novel classified self-organising map algorithm is proposed to solve the problem. First, according to the K-means algorithm, targets are classified into groups that are determined by the number of AUVs. Second, according to the self-organising map algorithm, AUVs are matched with groups. Third, each AUV is provided with the accessible order of the targets in the group. The novel classified self-organising map algorithm can be used not only to reduce the total energy consumption in a multi-AUV system, but also to give the most efficient accessible order of targets for AUVs. Results of simulations conducted to prove the applicability of the algorithm are given. [ABSTRACT FROM AUTHOR]

In this article, in order to solve the trajectory tracking control problem with the drive saturation (thrust overrun) for the 4500-m human occupied vehicle named “Deep-sea Warrior,” a model predictive adaptive constraint control strategy is put forward. The proposed control strategy mainly consists of two controllers. The first part is a kinematics controller designed by quantum-behaved particle swarm optimization model predictive control method. The second part is a dynamic controller designed by an adaptive algorithm. In order to study the effect of the ocean current disturbance on tracking controller, the ocean current is incorporated into the kinematics and dynamics model of the 4500-m human occupied vehicle. The thrusts of four degrees of freedom under the ocean current are calculated from designed controllers. Then, the thrusts are assigned to six thrusters on the 4500-m human occupied vehicle according to its thruster arrangement. An ocean current observer based on artificial fish proportional-integral control is designed for unknown currents. The simulation results of tracking control in three-dimensional underwater environment are given, which illustrates that the proposed control strategy can not only meet the hardware requirements (drive saturation) but also achieve a stable and efficient tracking control performance because of its constraint to speed and speed increment, the effect of the ocean current on kinematics and dynamics models and the dual feedback mechanism. [ABSTRACT FROM AUTHOR]

Due to the unique nature of underwater acoustic communication, data collection from the Underwater Acoustic Sensor Networks (UASNs) is a challenging problem. It has been reported that data collection from the UASNs with the assistance of the autonomous underwater vehicles (AUVs) will be more convenient. The AUV needs to schedule a tour to contact all sensors once, which is a variant of the Traveling Salesman Problem. A hybrid optimization algorithm is proposed for the solution of the problem. The algorithm combines the quantum-behaved particle swarm optimization and improved ant colony optimization algorithms. It is an algorithm with quadratic complexity, which can yield approximate but satisfactory results for the problem. Simulation experiments are carried out to demonstrate the efficiency of the algorithm. Compared to the Self-Organizing Map based (SOM-based) algorithm, it not only plans a shorter tour, but also shortens the distance from the sensor to its closest waypoint. Therefore, the algorithm can reduce the energy required for data transmission since the communication distance drops, and the service life of the sensor can be extended. [ABSTRACT FROM AUTHOR]

The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs. [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]

For multi-Autonomous Underwater Vehicle (multi-AUV) system task assignment and path planning, a novel Glasius Bio-inspired Self-Organising Map (GBSOM) neural networks algorithm is proposed to solve relevant problems in a Three-Dimensional (3D) grid map. Firstly, a 3D Glasius Bio-inspired Neural Network (GBNN) model is established to represent the 3D underwater working environment. Using this model, the strength of neural activity is calculated at each node within the GBNN. Secondly, a Self-Organising Map (SOM) neural network is used to assign the targets to a set of AUVs and determine the order of the AUVs to access the target point. Finally, according to the magnitude of the neuron activity in the GBNN, the next AUV target point can be autonomously planned when the task assignment is completed. By repeating the above three steps, access to all target points is completed. Simulation and comparison studies are presented to demonstrate that the proposed algorithm can overcome the speed jump problem of SOM algorithms and path planning in the 3D underwater environments with static or dynamic obstacles. [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]

This paper focuses on depth trajectory tracking control for a Remotely Operated Vehicle (ROV) with dead-zone nonlinearity and saturation nonlinearity of thruster; an adaptive sliding mode control method based on neural network is proposed. Through the analysis of dead-zone nonlinearity and saturation nonlinearity of thruster, the depth trajectory tracking control system model of a ROV which uses thruster control signals as system input has been established. According to the principle of sliding mode control, an adaptive sliding mode depth trajectory tracking controller is built by using three-layer feed-forward neural network for online identification of unknown items. The selection method and update laws of the control parameters are also given. The uniform ultimate boundedness of trajectory tracking error is analysed by Lyapunov theorem. Finally, the effectiveness of the proposed method is illustrated by simulations. [ABSTRACT FROM PUBLISHER]

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]

In this paper, for the over-actuated Autonomous Underwater Vehicle (AUV) system, a novel tracking controller with thruster fault accommodation is proposed. Firstly, a cascaded control method is proposed for AUV robust tracking control. Then, we deal with the tracking control problem when one or more thrusters are completely or partly malfunctioning. Different control strategies are used to reallocate the thruster forces. For the cases that thrusters are partly malfunctioning, a weighted pseudo-inverse is firstly used to generate the normalised thruster forces. When the normalised thruster forces are out of maximum limits, the Quantum-behaviour Particle Swarm Optimisation (QPSO) is used for the restricted usage of the faulty thruster and to find the solution of the control reallocation problem within the limits. Compared with the weighted pseudo-inverse method, the QPSO algorithm does not need truncation or scaling to ensure the feasibility of the solution due to its particle search in the feasible solution space. The proposed controller is implemented in order to evaluate its performance in different faulty situations and its efficiency is demonstrated through simulation results. [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]

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM. [ABSTRACT FROM AUTHOR]

A thruster fault tolerant control method based on quantum‐behaved particle swarm optimisation (QPSO) is investigated for 7000 m JIAOLONG manned submarine vehicle (MSV) in this study. First, QPSO is selected for the fast convergence and global search capability. To show the efficiency of QPSO fault tolerant method, PSO is studied as a comparison research. Then, to further apply the proposed method, tracking control problem with thruster fault is taken into consideration. Finally, simulations illustrate the performance of the derived fault tolerant tracking control technique. [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]

In this paper, a new approach of tracking control is investigated for the 7000-m Jiaolong manned submarine vehicle (MSV). First, the formulation of control allocation problem and some background information about tracking control of the 7000-m Jiaolong MSV are presented. Then, a kinematic controller is derived. The filter design using bioinspired model is employed to handle the speed jump problem and to make sure that each thruster is within the saturation limit. The kinematic controller is then extended to incorporate a sliding-mode control technique to complete the dynamic control. The system stability is guaranteed, and tracking errors asymptotically converge to zero by Lyapunov stability theory. Another interesting phenomenon is that the bioinspired method can achieve more satisfactory tracking result with a less control effort compared to a typical backstepping method. Finally, simulations illustrate the performance of the derived cascaded tracking control technique. [ABSTRACT FROM PUBLISHER]

A novel fuzzy control method is presented for AUV (Autonomous underwater vehicles) path planning in both static and dynamic three-dimensional environment. First, on the basis of the forward looking sonar model, the virtual acceleration and velocity of AUV in both horizontal and vertical plane can be gotten through the fuzzy system. Then the velocity synthesis approach is applied to generate the real control variables in the body-fixed frame. In addition, a fuzzy-inference system with an accelerate/break (A/B) module is developed for real-time navigation, which enables AUV to avoid dynamic obstacles automatically. Finally, simulation results indicate the effectiveness and feasibility of the proposed approach. [ABSTRACT FROM AUTHOR]

A biologically inspired neurodynamics-based tracking controller of underactuated Autonomous Underwater Vehicles (AUV) is proposed in this paper. The proposed control strategy includes a velocity controller with biological neurons and an adaptive sliding mode controller. The biological neurons are embedded into the backstepping velocity controller to eliminate the sharp speed jumps commonly existing in vehicles due to tracking errors changing suddenly. The outputs of the velocity controller are used as the command inputs of the sliding mode controller, and the thruster control constraints problems that are commonly seen in the backstepping control of AUV are solved by the proposed controller. Simulation results show that the control strategy achieved success in smoothly tracking AUV position and velocity. [ABSTRACT FROM PUBLISHER]

A novel thruster fault diagnosis method for open‐frame unmanned underwater vehicle is presented in the study. An credit assignment‐based fuzzy cerebellar model articulation controller (FCA‐CMAC) neural network information fusion model is used to realise the fault identification for thruster continuous and uncertain jammed fault situations. Information inputs for the fusion model are yaw rate and the control signal for the underwater vehicles; the information output of the FCA‐CMAC fusion model is the corresponding jammed fault parameter s, which indicates the degree of the fault. To illustrate the effectiveness of the proposed method, a pool experiment under uncertain continuous fault conditions is presented in this study. [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]

In this article, aiming at the characteristics of model uncertainty and low-speed of human-occupied vehicle (HOV) system, a cascaded controller in ocean current environment is proposed. First, the kinematics controller is designed by improved chaotic firefly algorithm (ICFA)-model predictive control (MPC) to get the speed control signal. The multivariable constraint capability of ICFA-MPC can solve the problem of speed jump caused by thruster fault and further solve the problem of driving saturation. Second, the kinematic controller is combined with the dynamics controller which is according to the adaptive algorithm. The adjustable control parameters capability of the adaptive control can further obtain the dynamic control law under the influence of ocean current. Finally, the hybrid allocation strategy based on ICFA is used to redistribute the thruster force/moments. Simulation results displayed the dynamic trajectory tracking fault-tolerant controller can steadily track the reference trajectory under thruster fault without tracking disturbance, speed jump, and driving saturation problem. [ABSTRACT FROM AUTHOR]

A novel hybrid control approach is presented for trajectory tracking control of unmanned underwater vehicles in this paper. The kinematic and dynamic controllers are integrated by the proposed control strategy. The paper has two objectives. Firstly, an improved backstep method is proposed to generate the virtual velocity using a bio-inspired neurodynamics model in the kinematic controller. The bio-inspired neurodynamics model is intended to smooth the virtual velocity output to avoid speed jumps of the unmanned underwater vehicle caused by tracking errors and to meet the thruster control constraints. Secondly, a new sliding-mode method is added to the dynamic controller, which is robust against parameter inaccuracy and disturbances. The combined kinematic-dynamic control law is applied to the trajectory tracking problem of two different types of unmanned underwater vehicle. Finally, simulation results illustrate the performance of the proposed controller. [ABSTRACT FROM AUTHOR]

A thruster reconfiguration control approach of manned submarine with 7000 m operation depth based on quantum-behaved particle swarm optimisation (QPSO) is presented in this article. The manned submarine has eight thrusters. When thruster faults happen, the corresponding weight matrix is updated to restrict the usage of the faulty thruster. But the solution with this method may become unfeasible (exceed the rated valve of the thruster) and cannot be directly applied to the thrusters. In order to complete an appropriate control law reconfiguration, a novel control reallocation method based on QPSO is proposed. Not only is the solution obtained by the approach of QPSO limited in the whole feasible space, but also the control error of the fault-tolerant control is very small. Eight dimensions of the particles are used in this article, and each particle represents the components of the control vector, it searches in the range of the restricted factor value to make sure that all the reconfiguration control solutions are feasible. Compared to the weighted pseudo-inverse method, the error of the obtained control vector with the QPSO is very small. Finally, simulation examples of multi-uncertain faults are given to illustrate the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

A model based on PCA (principal component analysis) and a neural network is proposed for the multi-fault diagnosis of sensor systems. Firstly, predicted values of sensors are computed by using historical data measured under fault-free conditions and a PCA model. Secondly, the squared prediction error (SPE) of the sensor system is calculated. A fault can then be detected when the SPE suddenly increases. If more than one sensor in the system is out of order, after combining different sensors and reconstructing the signals of combined sensors, the SPE is calculated to locate the faulty sensors. Finally, the feasibility and effectiveness of the proposed method is demonstrated by simulation and comparison studies, in which two sensors in the system are out of order at the same time. [ABSTRACT FROM AUTHOR]

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]

For a class of remotely operated vehicle (ROV) systems with thruster constraints, immeasurable states, and unknown nonlinearities, the trajectory tracking control problem was discussed in this paper. The unknown nonlinear functions were approximated by radial basis function (RBF) neural networks. An adaptive state observer based on neural networks was designed and the immeasurable states were estimated. Considering the problem of thruster saturation constraints, an auxiliary system for saturation compensation was designed and a saturation factor was constructed by the auxiliary system state. By applying the backstepping design method, an adaptive neural sliding mode trajectory tracking controller was developed, in which the saturation factor is contained in adaptive laws. It was proved that the uniformly ultimately bounded (UUB) of trajectory tracking errors can be obtained. Finally, the effectiveness of the proposed trajectory tracking control approach was checked by simulations. [ABSTRACT FROM AUTHOR]

In recent years, with the continuous advancement of the construction of the Yangtze River's intelligent waterway system, unmanned surface vehicles have been increasingly used in the river's inland waterways. This article proposes a hybrid path planning method that combines an improved A* algorithm with an improved model predictive control algorithm for the autonomous navigation of the "Jinghai-I" unmanned surface vehicle in inland rivers. To ensure global optimization, the heuristic function was refined in the A* algorithm. Additionally, constraints such as channel boundaries and courses were added to the cost function of A* and the planned path was smoothed to meet the collision avoidance regulations for inland rivers. The model predictive control algorithm incorporated a new path-deviation cost while imposing a cost constraint on the yaw angle, significantly minimizing the path-tracking error. Furthermore, the improved model predictive control algorithm took into account the requirements of rules in the cost function and adopted different collision avoidance parameters for different encounter scenarios, improving the rationality of local path planning. Finally, the proposed algorithm's effectiveness was verified through simulation experiments that closely approximated real-world navigation conditions. [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]

Abstract: Enzymatic pretreatments of oat bran, using viscozyme L to enhance protein extraction, were investigated. To obtain the best possible combination of enzymatic pretreatment variables including viscozyme L concentration, incubation time, pH and incubation temperature for maximum protein extraction, modern optimization techniques were employed. The optimization procedure included two stages: creating a response surface model through regression on experimental data and then optimizing the model by Particle Swarm Optimization (PSO) algorithms. The optimum conditions of enzymatic pretreatment were: viscozyme L concentration, 30FBG/10g of oat bran; incubation time, 2.8h; pH 4.6; and incubation temperature, 44°C. Under such conditions, the predicted maximum protein extracted was 55.69%, which coincided with the experimental values. These results indicated that enzymatic pretreatment can improve protein extraction significantly and PSO algorithms were efficient approaches for optimizing the conditions of enzymatic pretreatment of oat bran. [Copyright &y& Elsevier]

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]

Perceptual video coding (PVC) can provide a lower bitrate with the same visual quality compared with traditional H.265/high efficiency video coding (HEVC). In this work, a novel H.265/HEVC-compliant PVC framework is proposed based on the video saliency model. Firstly, both an effective and efficient spatiotemporal saliency model is used to generate a video saliency map. Secondly, a perceptual coding scheme is developed based on the saliency map. A saliency-based quantization control algorithm is proposed to reduce the bitrate. Finally, the simulation results demonstrate that the proposed perceptual coding scheme shows its superiority in objective and subjective tests, achieving up to a 9.46% bitrate reduction with negligible subjective and objective quality loss. The advantage of the proposed method is the high quality adapted for a high-definition video application. [ABSTRACT FROM AUTHOR]

This paper discusses parameter estimation problems of the multivariable systems described by input-output difference equations. We decompose a multivariable system to several subsystems according to the number of the outputs. Based on the maximum likelihood principle, a maximum likelihood-based recursive least squares algorithm is derived to estimate the parameters of each subsystem. Finally, two numerical examples are provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]