Search

The present work describes the constraint analysis methodology to perform initial sizing for generic wing-in-ground-effect (WIG) vehicles which can be used for their initial conceptual design and the design space exploration. Constraint formulations based on classical equations of motion were adapted for different types of WIG craft to obtain a compact set of equations to visually represent the feasible design space. Relations for classical WIG, power-augmented WIG craft (PARWIG), and dynamic air cushioning WIG craft (DACWIG) were developed to account for various vehicle configurations and model their specific features. Apart from theoretical and available semi-empirical formulations for WIG craft, additional constants and correction factors were recommended using verified time-dependent simulations of several reference WIG craft to enhance the accuracy of the method and perform verification studies. Constraint diagrams and corresponding design spaces were compared to existing data of WIG craft and demonstrated strong capabilities of the method to create the design space with reasonable wing loadings and thrust (power)-to-weight ratios. Finally, directions to apply, improve, and further verify the formulations are suggested. • The constraint analysis methodology was developed for wing-in-ground effect vehicle conceptual design. • The constraint analysis methodology was developed for WIG, PARWIG, and DACWIG craft sizing. • The methodology was applied to seven reference cases and showed acceptable accuracy for all vehicles. [ABSTRACT FROM AUTHOR]

The paper proposes a fleet size and mix vehicle routing model to optimize the river-sea liner shipping service. A mixed integer linear programming model is adopted as a rapid screening tool for strategic fleet planning to determine the type and number of river-sea vessels to deal with the fluctuation of the demand over the year, guaranteeing the efficiency and effectiveness of the shipping service. Moreover, the model provides a better-informed decision on the logistic infrastructure design, demand allocation and fleet size and composition in scenarios of demand uncertainty. Thus, the quantified decision support model increases the service level of the shipping service, reducing the frequent mismatches between fleet capacity and demand. A verification step aiming to test the behaviour of the model solution in a realistic case study is presented to analyse the impact of the demand fluctuation on the fleet composition. In conclusion, river-sea vessels enable door-to-door transport via coastal and inland waterways without transhipment. This approach offers a reliable and cost-effective solution. The findings of the work may enhance the state of the art, expanding the narrow studies on river-sea liner shipping services and demonstrating the advantages of river-sea vessels. • The paper proposes a fleet size and mix vehicle routing model to optimize the river-sea liner shipping service. • A mixed integer linear programming is adopted as a tool for strategic fleet planning i.e. the type and number of vessels. • A decision support model increases the level of the shipping service reducing mismatches between fleet capacity and demand. • A verification step aiming to test the behaviour of the model solution in a realistic case study is presented. • The river-sea transport without transhipment was shown to be a sustainable, reliable, and cost-effective. [ABSTRACT FROM AUTHOR]

In the existing research on supercavitating vehicle control, the assumption of constant forward speed places enormous pressure on the engine under actual operating conditions. For the convenience of realization, a control problem for a supercavitating vehicle under constant thrust is studied. To evaluate the effects of the changes in the cavitation number, nonlinear planing force, fin efficiency perturbation and other uncertainties, an affine linear parameter varying (LPV) model is established, and an affine robust guaranteed cost controller is designed. In the process of designing the controller, a set of time-varying guaranteed cost constraints for the uncertainties in the planing force, forward speed, etc., is presented. Among them, the cost function is designed to reduce the actuator saturation and improve the tracking performance. To find feasible solutions to the constraints under the premise of low conservatism, a simple convex optimization method is introduced. Simulation experiments are carried out for the ideal environment and the environment that contains uncertainties, such as fin efficiency perturbations and pressure sensor noise. The results prove that under the configuration of this control method, the system has less steady-state error, a fast dynamic convergence rate, and a superior robust performance of stabilizing uncertainties and disturbances. • A new constant thrust supercavitating vehicle model is established. • The dynamics characteristics of constant thrust vehicles is discussed. • A new guaranteed cost controller based on LMI approach for LPV systems is carried out. [ABSTRACT FROM AUTHOR]

Dynamic window approach (DWA) considering dynamic performance provides good results in local path planning of autonomous underwater vehicle (AUV). However, the path planned by DWA sometimes detours, and the dynamic performance is limited by constants unreasonably. In this paper, an improved method called vector field adaptive DWA (VADWA) is proposed. Firstly, adaptive parameters and vector field histogram are introduced to solve the problem of routing detouring, so the evaluation parameters and evaluation system of DWA are both optimized, which significantly improves the obstacle avoidance efficiency of AUV. Secondly, different from the practice of simplifying the dynamic model to constants in DWA, VADWA considers the real dynamic model and the input characteristics of actuator to evaluate the dynamic performance of AUV, so as to give more reasonable planning signals. Finally, aiming at the problem of the desired state tracking of AUV in complex environment, a velocity controller is designed to form a complete hierarchical planning-control scheme, which realizes the stable and accurate tracking of desired state. Simulation results show that VADWA can effectively shorten the distance and time for AUV to reach the target point, and generate control instructions that are more consistent with dynamic characteristics of AUV. • An adaptive dynamic window approach is proposed to improve the flexibility of AUV local path planning. • The vector field histogram is introduced into the dynamic window approach to optimize the evaluation system of the algorithm. • Considering dynamic model of AUV and characteristics of actuator, the dynamic performance of AUV is evaluated dynamically. • A complete hierarchical planning-control scheme of AUV is formed, which greatly facilitates AUV velocity tracking. [ABSTRACT FROM AUTHOR]

High-precision online modeling is the basis for various marine control and navigation of surface vehicles. An incremental Gaussian processes learning based on subspace index and adaptive spectral metric kernels is proposed for incremental online non-parametric identification of surface vehicle dynamics. The scheme utilizes streaming data for online identification without special designs of training data and can consider environmental interference. The subspaces are updated online by similarity, so the proposed subspace index can be viewed as a sparse technique for streaming online identification data. In order to improve the online model accuracy, an adaptive version of spectral metric kernels is designed in parallel utilizing novel adaptive moment estimation. The feasibility of the proposed scheme is verified through random steering tests of a 4-degree-of-freedom surface vehicle. Compared with traditional non-parametric identification methods, the proposed online identification scheme is effective in prediction accuracy and model generalization. The results show that this online scheme can provide a model basis for intelligent surface vehicle automation. • An incremental online non-parametric modeling scheme is constructed to identify surface vehicle dynamics online. • A spectral metric kernel is designed for incremental Gaussian processes learning to improve online identification accuracy. • A subspace index method is proposed to improve the efficiency of the online identification scheme. [ABSTRACT FROM AUTHOR]

The sustained impact load on vehicle entering the water obliquely at high speed can cause structural damage and complex ballistic characteristics. In this paper, a multi-stage load reduction structure (MLRS) combining aluminum foam and a disk cavitator is designed. A three-dimensional numerical model is established using the CEL method to study the high-speed oblique water entry impact of a vehicle. The validity of the numerical method is then verified. Based on numerical models of the vehicle and the multistage loading reduction structure, this study investigates the dynamic characteristics of the vehicle when entering the water directly and when using a MLRS, under different speed conditions and entry angles. The effects of the MLRS on the impact load and attitude behavior of the vehicle under various parameters are studied. The results show that the impact acceleration becomes more prominent with a higher initial velocity when oblique entry. Additionally, smaller entry angle causes the projectile to experience a more significant deflection in its ballistic trajectory. The MLRS designed in this paper can effectively reduce impact loads and modify the characteristics of cavity evolution and trajectory. Moreover, it has a better load reduction effect at a larger access angle. • The paper titled "Numerical study on the dynamic characteristics of a vehicle with a multistage load reduction structure during oblique water entry" contains those highlights: • In this study, a multistage load reduction structure (MLRS) is proposed, comprising a cone cavitator, aluminum foam, and a stretchable bar. • This study investigates the behavior of a vehicle entering water with the MLRS, focusing on the impact loads and trajectory characteristics under various entry velocities and angles. • The designed MLRS demonstrates effective reduction of impact loads and improvement in trajectory stability, particularly in cases involving large angles and high speeds. [ABSTRACT FROM AUTHOR]

During the process of high-speed water entry, the vehicle will bear large loads. Obtaining and analysing the time-frequency characteristics of loads is of great engineering significance for the protection of shell and equipment. The high-speed camera is used to capture the evolution of cavity and the accelerometer is used to collect the acceleration of model during water entry. The time-frequency characteristics of acceleration are obtained based on the short-time Fourier transform and discrete wavelet transform. The results indicate that the cavity exhibits an asymmetric shape and shrinks near free surface during the oblique high-speed water entry. When the cavity undergoes closure and collapse, the collapsing order of the upper and lower parts is opposite. Through the short time Fourier transform, it was found that the loads contain components of different frequency at different periods. When the model collides with free surface, the frequency contained in acceleration are more complex, reflecting the complexity of mechanical environment. The discrete wavelet transform is used to extract the components with more time-resolution and obtain the relative energy of each component. It is found that the relative energy of low-frequency components increases with the increase of water entry speed. • The high-speed water entry experiments were conducted on a vehicle model. • The process of cavity change was captured by using a high-speed camera. • The data of impact acceleration was collected by using an acceleration sensor. • The time-frequency domain characteristics of impact acceleration were obtained. [ABSTRACT FROM AUTHOR]

This paper presents a novel tracking control method based on a collision-free flexible performance function (CFPF) that allows us to preset tracking performance bounds for uncertain underactuated surface vehicles in the presence of moving obstacles and input saturation. In contrast to previous studies that have utilized flexible performance functions, we introduce a new CFPF combined with alteration signals to adjust the tracking performance against moving obstacles with unknown velocities. The CFPF-based adaptive control design strategy is established using the coordinate transformation method and auxiliary variables, where the CFPF tuning mechanism is derived from a Lyapunov-based tracker design. By employing the Lyapunov stability theory, we prove that the proposed method allows for the desired tracking performance adjustment, thereby ensuring trajectory tracking and obstacle avoidance despite model uncertainty, input saturation, and unknown moving obstacles. • A tracking control using a collision-free flexible performance function (CFPF) is developed. • A novel CFPF is derived to adjust prescribed performance against moving obstacles. • The stability of the CFPF-based adaptive tracking system is analyzed mathematically. [ABSTRACT FROM AUTHOR]

A robust model predictive control (MPC) method with dual closed-loops is presented to handle trajectory tracking of autonomous underwater vehicle (AUV) with uncertain model parameters and random external perturbations. First, constraint conditions are set for the motion state and control input of the underwater vehicle based on its motion characteristics. The position controller takes the velocity increment as input, thus providing a smoothly varying desired velocity for the velocity controller. The velocity controller comprises nominal MPC and a nonlinear auxiliary control law to overcome the effect of random perturbations on AUV tracking control. Then, a finite-time extended state observer (FTESO) is designed to compensate for dynamic model uncertainty. Furthermore, Lyapunov stability theory is employed to analyse the stability of the controller and FTESO. Ultimately, through comparative simulation experiments, the proposed control framework's effectiveness and robustness are verified, proving it to be a feasible AUV trajectory tracking control method. • A dual closed-loop 3D trajectory tracking control framework based on tube MPC is designed. • A finite time extended state observer is designed to estimate and compensate for the uncertainty of the AUV dynamic model. • By designing a nonlinear auxiliary control law, the influence of external disturbances on AUV is solved, and the robustness of system is improved. • The stability and feasibility of the state observer and tube MPC-based controller are rigorously demonstrated. [ABSTRACT FROM AUTHOR]

A robust model predictive control (MPC) method with dual closed-loops is presented to handle trajectory tracking of autonomous underwater vehicle (AUV) with uncertain model parameters and random external perturbations. First, constraint conditions are set for the motion state and control input of the underwater vehicle based on its motion characteristics. The position controller takes the velocity increment as input, thus providing a smoothly varying desired velocity for the velocity controller. The velocity controller comprises nominal MPC and a nonlinear auxiliary control law to overcome the effect of random perturbations on AUV tracking control. Then, a finite-time extended state observer (FTESO) is designed to compensate for dynamic model uncertainty. Furthermore, Lyapunov stability theory is employed to analyse the stability of the controller and FTESO. Ultimately, through comparative simulation experiments, the proposed control framework's effectiveness and robustness are verified, proving it to be a feasible AUV trajectory tracking control method. • A dual closed-loop 3D trajectory tracking control framework based on tube MPC is designed. • A finite time extended state observer is designed to estimate and compensate for the uncertainty of the AUV dynamic model. • By designing a nonlinear auxiliary control law, the influence of external disturbances on AUV is solved, and the robustness of system is improved. • The stability and feasibility of the state observer and tube MPC-based controller are rigorously demonstrated. [ABSTRACT FROM AUTHOR]

The supercavitating vehicle is a new generation of high-speed underwater vehicles enveloped by a large bubble of gas or vapor (i.e., a supercavity) to reduce navigation resistance by an order of magnitude, except for the head and tail. However, due to the limited hydrodynamic forces, it is difficult to ensure stable motion. The overall shape of the vehicle is not only the critical factor to ensure a suitable relative position between the vehicle and its supercavity for stable motion, but also directly affects the ease of generation and development of the supercavity. Therefore, this study focuses on the characteristic parameters of the overall shape of the vehicle and first establishes a multi-objective optimization model based on the dynamic model of the supercavitating vehicle with the incipient supercavitation number and the vehicle volume as the optimization objectives. Then, given the relatively good global, robust and universal characteristics of the adaptive genetic algorithm compared to other algorithms, a multi-objective optimization algorithm is developed and improved using the subsection optimization method for the main optimization variable. The characteristic parameters of the overall shapes are optimized for natural and ventilated supercavitating vehicles using the algorithm based on the multi-objective optimization model so that it is easier to create a supercavity under the conditions of the same flow and force equilibrium and the vehicle has a larger volume. By optimizing the vehicles under different navigation depths and slenderness ratios, the design rules for the characteristic parameters of the overall shape and the control surface parameters are obtained based on the optimization objectives, and the motion state and hydrodynamic characteristics of the optimized vehicles are revealed. • A multi-objective optimization model is established for the SCAV's overall shape. • An adaptive multi-objective optimization algorithm is improved and developed. • Natural and ventilated SCAVs' overall shapes are optimized to achieve the optimal solution sets. • Design rules for the SCAV's overall shape and control surface parameters are obtained. • Motion state and hydrodynamic characteristics of the optimized SCAVs are revealed. [ABSTRACT FROM AUTHOR]

To improve the endurance and speed of an autonomous underwater vehicle (AUV), we consider adopting the slip over a solid-liquid interface, which has the effect of drag reduction. However, the simulation of motion of an AUV with the slip remains a challenge due to the fluid-structure interaction and multiscale problems involving the moving boundary. To solve these problems, the present work proposes a novel and competitive mesoscopic method. Firstly, the Discrete Unified Gas Kinetic Scheme (DUGKS) coupled with the immersed boundary method is improved by correcting the boundary force, avoiding any additional computational time or implementation effort. Its effectiveness and accuracy are validated by two numerical tests involving the moving boundary. Furthermore, a fluid-solid interaction force is introduced to this method to model the slip condition due to its easy and effective implementation. We perform the experiment of motion of an AUV model in water to further validate the proposed method, which shows that it can predict acceptable results. Then, the method is applied to study the effect of the slip on the motion of the AUV model. The greater slip induced by the fluid-solid interaction force has a greater effect on reducing the drag and improving the velocity. It is indicated that increasing the fluid-solid interaction force can increase the amplitude and decrease the oscillation frequency of the drag curve. Overall, the slip can improve the endurance and speed of the AUV model. • An immersed boundary-discrete unified gas kinetic scheme is improved by correcting the boundary force. • A fluid-solid interaction force is introduced to IB-DUGKS to model the slip condition. • The experiments shows that the proposed method can predict acceptable results. • The proposed method is applied to study the effect of the slip on the motion of the AUV model. [ABSTRACT FROM AUTHOR]

The supercavitating vehicle are capable of achieving high speed through cavitation reduction technology, however, the model of the vehicle is inferior stability and nonlinear dynamics. Inferior stability is mainly reflected in the sensitivity to the interference. The strong nonlinear characteristics embodies in the special planing force caused by water-cavity interaction on the aft part of the vehicle. Regarding to the inferior stability and strong nonlinear dynamics of the supercavitating vehicle, sliding mode controllers are presented in this paper. By using boundary sliding mode control, boundary of nonlinear planing force is estimated. In order to reduce the switch gain of the boundary sliding mode controller, the sliding mode controller based on disturbance observer is proposed. The disturbance observer is designed. The stability of the system is proved by Lyapunov stability theorem. Evaluations results through simulation on tracking performance of the nominal and uncertainty system with time delay show the effectiveness of the control method. • The ordinary sliding mode controller, boundary sliding mode controller and continues sliding mode controller based on disturbance observer are designed for the supercavitating vehicle. • The Lyapunov theorem is exploited to prove the system stability. • The step responses and tracking responses are conducted in the simulation, and the time delay is added in the simulation. [ABSTRACT FROM AUTHOR]

Abstract In this paper application of a nonlinear tracking control algorithm for dynamics testing of underwater vehicles is presented. The controller, suitable for fully actuated vehicles, is based on a velocity transformation which results from the inertia matrix decomposition. After use of the transformation some properties of the system dynamics are observable. The matrices containing the dynamical parameters set are included next into the control gain matrices. Consequently it is possible, introducing a set of indexes, to propose a procedure for dynamics investigation. The approach can serve as a useful tool before experiment because it gives an insight into the vehicle dynamic model. The simulation studies demonstrated on a 6-DOF underwater vehicle model show effectiveness of the given strategy. Highlights This work deals with the problem of the use of a trajectory tracking algorithm for numerical dynamics study of underwater vehicles. Stability analysis of the system under the control algorithm is done using on the Lyapunov's direct method. Application of the presented approach is tested on a model of an underwater vehicle. Research area • Marine vehicles control. • Dynamics investigation. • Model numerical testing. • Stability analysis based on Lyapunov direct method. [ABSTRACT FROM AUTHOR]

An adaptive neural network with nonlinear fractional-order PID (ANNFOPID) controller design is proposed for underwater robotic vehicle (URV) to solve the path tracking problem. The path tracking problem is caused by disturbances and unknown uncertainties of the underwater vehicle dynamic model. The disturbances were presented with the URV model to evaluate the performance of the ANNFOPID controller to reject the disturbances. At the same time, an obstacle avoidance model has been presented with ANNFOPID controller to evaluate the controller ability to maneuver around different obstacle locations. A radial base function (RBF) has been used to estimate both of the disturbances and the unknown uncertainties of the dynamic model. An improved slime mould algorithm (ISMA) is presented to invent a new trajectories for the underwater vehicle and enhance the ability of the URV to overcome the obstacles problems. At the end, the results obtained show that the ANNFOPID controller present an outstanding performance in comparison with other existing works to overcome the disturbances, unknown uncertainties, and obstacles problems effectively. • A new controller design has been proposed to estimate the disturbances, unknown uncertainties, and avoid the obstacles. • The proposed controller is presented to provide the robustness for the URV to overcome the external disturbances effects. • An improved algorithm has been presented with the proposed controller to generate new optimized trajectories for the URV. • The collected results prove the superiority of the proposed controller in comparison with other existing works. [ABSTRACT FROM AUTHOR]

This study aimed to develop a deep-sea mining vehicle (DSMV) path-following controller that could better reflect the actual deep-sea mining conditions. First, the dynamic model of the DSMV was improved. By introducing a nonlinear slip-control model and random environmental noise resistance, the controlled plant was developed to be closer to the actual mining operation condition. Second, an improved deep deterministic policy gradient (IDDPG) algorithm was proposed. Compared to the standard DDPG algorithm, the improved algorithm reduces the overestimation of the Q value and enhances the ability of an agent to explore the global optimum. A warm-up stage was introduced to improve stability at the beginning of training and accelerate the convergence speed of training. Third, a general reward function was designed for this type of problem. Combined with the uncertainty of the improved model, the generalization ability and adaptability to the unknown environment of the controller could be improved. Finally, through a random one-point-following training test in the simulation environment and different path-following comparison tests, the path-following control ability of the controller was verified. • Improved deep-sea mining vehicle model includes slip-control and noise resistance for nonlinear dynamics. • Proposed deep RL controller has continuous output and general reward functions for path following. • Improved controller's path-following ability verified via straight-line, S-curve, and standard path-following tests comparison. [ABSTRACT FROM AUTHOR]

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. • A feasible guidance trajectory based on the mobile DS is generated through using geometrical analysis method, which provides a reference trajectory for the design of the tracking controller. • A novel adaptive integral event-triggered nonlinear model predictive control (AIET-NMPC) is designed to reduce the computational burden. • Aiming at the trajectory tracking NMPC optimization problem, a new exponential robust constraint of system state is proposed to improve the stability margin of the closed-loop system. • The sufficient conditions to ensure the algorithm feasibility and system stability are strictly derived. [ABSTRACT FROM AUTHOR]

Dynamic ocean environments are difficult to monitor and affect the motion balance of underwater vehicles. Existing unsupervised environment perception methods have poor performance and less consideration of environment and vehicle interaction effects. This paper proposes a novel unsupervised time–frequency environment perception model for underwater vehicles in the irregular ocean. First, the observed ocean information is transformed into the time–frequency space based on the wavelet transform. After that, an adaptive dual-threshold diagnosis model is proposed to automatically extract and locate ocean features and depress the background noise. Then, the environment-vehicle interaction model is derived to quantitatively predict the state change of underwater vehicles caused by environmental features. Finally, extensive experiments on the novel buoyancy-driven underwater glider and publicly South China Sea dataset are conducted to verify the effectiveness of the proposed method. The result shows that the diagnosis model can better perceive the ocean environment, and the performance outperforms traditional unsupervised statistical and supervised learning methods. The thermocline detection accuracy is 100%, and the location accuracy is 98.73%. The environment-vehicle interaction model can accurately describe the state changes of underwater vehicles under environmental influence, with an average accuracy of 94.88%. • The unsupervised environment perception model is designed for underwater vehicles. • The adaptive dual-threshold diagnosis model is proposed for feature extraction. • The environment-vehicle interaction model is derived for motion control. [ABSTRACT FROM AUTHOR]

The hydrodynamic modeling of a surface vehicle in an aquatic environment is known to be a challenging problem. In particular, it is difficult to calculate the hydrodynamic forces due to the intricate coupling of the vehicle with water. In recent years, deep dynamics models have been utilized to improve the accuracy of dynamics modeling precision. However, it is well known that data-driven approaches do not extrapolate well and are vulnerable to out-of-distribution data. In this paper, we argue that the naive use of neural networks may reduce the accuracy of long-term predictions. In order to address this issue, we employ representation learning techniques that facilitate the learning of the valid data space, so that long-term predictions can be made within the valid data space. In addition, hallucinated replay is incorporated into the prediction network to further improve the accuracy of long-term predictions. We validate the proposed method on experimental data using a robotic surface vehicle and demonstrate its application to path tracking control. • Data-driven hydrodynamic modeling for a small robotic surface vehicle is attempted. • Representation learning improves long-term prediction of neural network-based model. • Data visualization and an out-of-distribution classifier verify the proposed model. • With a model-based RL framework, path tracking control is successfully demonstrated. [ABSTRACT FROM AUTHOR]

In the current study, the ventilated cavitating flow for a model near the free surface is investigated experimentally and numerically. The experiments are performed in a towing tank equipped with a ventilation system, drag measurement interface and a photography system. The numerical simulation is performed by commercial CFD code ANSYS-CFX with the volume of fluid (VOF) method, the RNG k-ε turbulence model, and effectiveness of the gravity. The ventilated supercavity and drag coefficient of the model under free surface effect is studied at different submergence depths and velocities. Both the experimental observations and numerical predictions show that with increasing the Froude number (Fr), the ventilated supercavity size increases and the drag coefficient decreases. The good agreement observed between the numerical predictions and experimental data, in terms of both the supercavity size and drag coefficient, has revealed the accuracy and capability of the applied numerical model. The numerical results reveal that with increasing the submergence depth, the drag coefficient increases due to existence of the cavitator at the front of the vehicle. Also, in the regions near the free surface, the variation of supercavity length with Fr is different from that of the regions away from the free surface. • With increasing the Froude number, the ventilated cavity size increases and the drag coefficient decreases. • The drag coefficient values in the cavitating flow is considerably lower than the non-cavitating flow. • Unlike the typical vehicles, with increasing the submergence depth, the drag coefficient is increased. • In the regions near the free surface, after a certain value of Froude number, not only the supercavity length does not increase, but also slightly decreases. • Unlike fully submerged condition, the supercavity length decreases with decreasing the submergence depth. [ABSTRACT FROM AUTHOR]

This article presents an event-triggered adaptive disturbance rejection scheme for marine surface vehicles with unknown model dynamics and external disturbances. The ocean environmental disturbances are expressed as a series of sinusoidal superpositions with unknown frequencies, amplitudes and phases. By incorporating the adaptive internal model principle, the disturbance rejection is transformed into the adaptive control problem. The adaptive laws and disturbance filter achieve online disturbance estimations. The adaptive neural backstepping is employed to derive the trajectory tracking control law. The event-triggering mechanism is utilized to reduce the wear and tear of thrusters. The event-triggered adaptive disturbance rejection control does not require a priori knowledge of model dynamics and can be treated as a model-free disturbance estimation and rejection scheme. It is theoretically demonstrated that the closed-loop tracking control system is semi-globally stable and the tracking error can be adjusted to be small. Illustrative results on a 1:70 scaled model CyberShip II validate the control scheme. • Adaptive internal model principle transforms disturbance rejection into adaptive control. • Model-free disturbance online estimations does not require a priori vehicle dynamics. • Event-triggering protocol is utilized to reduce the wear and tear of vehicle thrusters. [ABSTRACT FROM AUTHOR]

This paper presents a path-following and collision avoidance system for autonomous surface vehicles based on nonlinear model predictive control. The proposed strategy is capable of following a desired path while maintaining a commanded velocity, whereas it can diverge from the references to safely perform maneuvers to elude unexpected obstacles. A nonlinear dynamic model of the vehicle is applied to predict the vehicle states into a finite horizon. Furthermore, a LiDAR sensor located at the front of the boat is employed for local object detection. Here, the high-performance optimal control framework, acados, is used to solve the optimization problem onboard. Real-time numerical simulations and field experiments demonstrate the effectiveness of the proposed approach against multiple buoys. • An NMPC-based path-following and collision avoidance system for ASVs is proposed. • 3D LiDAR is applied to detect obstacles for evasion. • An efficient implementation is achieved using the optimal control framework, acados. • Simulations and physical experiments show the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper studies the fuzzy finite-time reliable dynamic positioning (DP) control for nonlinear unmanned marine vehicles. Firstly, the motion dynamics of nonlinear unmanned marine vehicles is modeled by the Takagi-Sugeno (T-S) fuzzy system. Secondly, a fuzzy state observer is designed to estimate the immeasurable position and velocity of unmanned marine vehicles. In order to decrease the frequency of data transmission of actuators and sensors, a dynamic event-triggered mechanism is presented based on unmanned marine vehicles information and the estimation error. Then, a fuzzy event-triggered reliable output feedback control method is presented by finite-time theory to tackle the actuator faults problem occurring in unmanned marine vehicles. The sufficient finite-time stable conditions in the form of LMIs are established via constructing finite-time Lyapunov-Krasovskii functional. Ultimately, the computer simulation results and comparisons with the existing control methods verify the advantages of the presented control method. [ABSTRACT FROM AUTHOR]

In this paper, a custom built, underactuated, inspection class micro Remotely Operated Unmanned Underwater Vehicle (ROV) is employed as a testbed to investigate control and modelling problems related to underwater vehicles in shallow waters and pools. Dynamic model for yaw is obtained via mathematical modelling and system identification techniques. To instil confidence in the identified model, residuals and cross-validation tests are carried out to obtain high fidelity vehicle model for subsequent stabilizing closed-loop control design. Following the modelling exercise, design, real-time implementation, and analysis of a GA optimized PI controller for yaw is carried out. The performance of the GA optimized controller is benchmarked against the experimental results of a multi-parameter root-locus tuned PI controller and simulated responses of a standard linear quadratic regulator (LQR) controller. In addition, the efficacy of the GA-PI controller is gauged employing recently developed marine predator algorithm (MPA). The need for a controller with optimized performance motivated the utilization of GA and MPA optimization techniques. The results from real-time pool experiments indicate substantially enhanced performance of GA optimized controller, outperforming other controllers by as much as 22% in performance indicators such as settling time and maximum overshoot. Furthermore, the GA optimized controller demonstrated far better robustness and disturbance rejection capabilities. • An inspection class ROV is designed build and instrumented capable of operating in shallow waters. • Modelling and control aspects are investigated on a custom-built vehicle as opposed to expensive off-the-shelf vehicle. • High fidelity yaw dynamic models for inspection class ROV are developed via mathematical modelling and system identification techniques. • Multi-parameter root-locus is employed as a benchmark controller to compare the performance with GA optimized PI controllers. [ABSTRACT FROM AUTHOR]

Faced with underwater missions within fixed area, unmanned underwater vehicles (UUVs) are often subject to environmental position constraints and their internal velocity constraints. This paper focuses on the trajectory tracking control problem of the UUVs with position-velocity constraints (PVCs) and model uncertainties. Different from the existing works, a novel adaptive position-velocity constrained tracking controller is designed for the UUVs. First, a nonlinear transformation function is employed on UUVs to facilitate directly constraining the position and velocity. Second, adaptive radial basis function neural networks (RBFNNs) are utilized to approximate the model uncertainties of the UUVs. By means of the Lyapunov stability theory, it is proved that the proposed control scheme can ensure all signals of the UUVs are semi-globally bounded and the PVCs are strictly maintained. Finally, both simulation and experiment results are given to validate the effectiveness and practicability of the proposed control scheme. • The safety is ensured by proposed adaptive position-velocity constraint controller. • The controller can estimate and compensate unknown hydrodynamic and modeling errors. • The result is verified by mathematical analysis, simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper presents an energy-based trajectory tracking control for a fully actuated underwater vehicle using a Hamiltonian-like framework and an asymmetric bounded control input. The following controller design criteria are addressed: effect of thruster faults, disturbances, and model uncertainties of the vehicle. The main ingredient for the design of the proposed controller has been a time-delay estimation (TDE) approach. Global asymptotic stability is ensured when the above controller design criteria are not considered in the plant model, and the boundedness of the closed-loop system variables is achieved when they are present and influence its behavior. Simulations are shown to illustrate the performance of the proposed controller on an underwater vehicle model. • A Time-Delay Estimation (TDE) is proposed with a Hamiltonian-like framework. • The TDE is used to estimate the effect of disturbances, actuator faults, parametric uncertainties and input saturation. • The bounded states of the system are guaranteed via convergence analysis. [ABSTRACT FROM AUTHOR]

Stowage planning is one of the most difficult and important operational tasks in liner shipping. To help ship operators make stowage plans safer, this paper presents a semi-automated approach to creating stowage plans for Roll-on/Roll-off (Ro-Ro) ships. Based on the ship's design data, accurate two-dimensional (2D) models of vehicle decks for stowage planning are established firstly. After the vehicle types for different stowage areas have been specified manually, a more realistic stowage plan is generated using an improved lowest horizontal line searching algorithm, which maximizes the deck utilization with the constraints of obstacles and ramps. Then, the ship's flotation, stability and strength under the loading plan are assessed by using a practical method verified by Lloyd's Register (LR) test cases. Finally, computational tests on a 3800 Pure Car and Truck Carrier (PCTC) are performed and discussed, showing that the proposed method can make comparable stowage plans to that given by the ship's final loading manual and can be used onboard to improve navigation safety. [Display omitted] • Accurate models of vehicle decks of Ro-Ro ships are established. • An improved lowest horizontal line searching algorithm is proposed. • More realistic stowage plans for Ro-Ro ships are generated. • Ship stability under the stowage plan is assessed using a practical method. [ABSTRACT FROM AUTHOR]

Efficient low-speed Autonomous Underwater Vehicles (AUV) could adopt a propulsion mechanism with a biomimetic approach of caudal fin propulsion. The developments of the biomimetic AUV have very little attention to the systematic enhancement scheme of the fin performance. Therefore, many unclear relations of the parameters in thrust generation and efficiency of the various types of biomimetic fins. The present study proposes a systematic enhancement scheme of the static and dynamic performances of the biomimetic fins in a low-speed regime. This scheme involves rigid, cupping, and flexible single-joint fins of the acceleration-reaction fin mechanism and a high aspect ratio lunate shape two-joint fin of the lift-based fin mechanism. Average net-force and Thrust-to-Power Ratio evaluated the static performance, while the estimated cruising speed characterized the dynamic performance. The present study shows that the flexible single-joint fin improved the maximum values of the rigid fin performance by three times in average net-force, five times in thrust-to-power ratio, and two and a half times in estimated cruising speed. The two-joint fin enhanced the static and dynamic performances of the flexible fin by three times in average net-force, four times in thrust-to-power ratio, and one and a half times in estimated cruising speed. The present study suggests that flexibility has a role as a thrust vectoring factor. Furthermore, the lift-based mechanism in the two-joint fin provides effective and efficient thrust generation. In the present study, the two-joint fin was the effective and efficient fin propulsion mechanism in a low-speed regime. • This study proposes a systematic enhancement scheme of the biomimetic fins. • The scheme is particularly for biomimetic AUV in a low-speed flow regime. • This study suggests that flexibility has a role as a thrust vectoring factor. • The lift-based mechanism outperforms the acceleration-reaction mechanism. • This study proposes a vector composition method to analyze the enhancement of the biomimetic fin. [ABSTRACT FROM AUTHOR]

A submersible multi-state vehicle (SMV) has been designed as an unmanned surface vehicle with global deployment capability. It can switch between several states including underwater, semi-submerged, and surface states. Free-running tests on the self-propelled model are performed to validate the multi-state transition principle of the SMV, with kinematic and hydrodynamic parameters recorded during the tests. Three-dimensional force sensors are designed to measure the wing forces, which provide insights into the mechanism of the state transition process. A series of tests conducted using program control methods verify the feasibility of the multi-state sailing and continuous state transition of the SMV. Appropriate threshold values for the wings to control depth and trim angle of the vehicle are determined through case studies on the dynamic performance. The wing forces measured show extraordinarily different characteristics and functions in different states, which provides useful information for further improvements to the design. • The self-propelled model of a submersible multi-state vehicle featuring wings and ballast tanks is proposed. • The mechanism of multi-state transition relies on the balance between the hydrodynamic and hydrostatic loads. • The hull is the main source of the hydrodynamic forces in the state transition process rather than the wings. • The threshold values of the depth and trim angle for the wings are crucial for realizing the multi-state transition. [ABSTRACT FROM AUTHOR]

The controllability and maneuverability of an Autonomous Underwater Vehicle (AUV) in practical applications need to be properly validated and assessed before the prototype is finalized for manufacturing. With regard to mathematical system model of the AUV, hydrodynamic coefficients have a dominant effect on the quality of vehicle pre-testing and evaluation, which is crucial to be estimated with adequate accuracy to curb the uncertainty from modeling simplifications. The standard time domain discrete Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) algorithms are two promising numerical approximation approaches for hydrodynamic identification technique with favourable computational complexity and acceptable estimation precision. In this paper, a study with respect to time domain discrete optimized UKF (OUKF) algorithm based on recursive tuning rule and update gradient descent lemma for a typical prototype known as NPS AUV II will be proposed to enhance adaptability and prediction performance of the identification approach with appropriate verification. In addition, Auto Regressive Moving Average (ARMA) noisy model needs to be included into three Kalman Filter (KF) algorithms to further improve the estimation precision and perturbations inhibiting performance. Pre-processing numerical model validation and non-dimensional viscous linear damping coefficient identification based on three KF algorithms will be implemented respectively to provide an assistant pre-assessment for the vehicle. In accordance with comparable outputs and estimation-experiment errors, the OUKF identification algorithm is certified to be more precise and superior compared with EKF and UKF approaches in the presence of ARMA noisy model. • Augmented system model for typical prototype NPS AUV II will be mathematically hypothesized and demonstrated. • Single estimation circle of Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) will be explained in detail. • Optimized UKF (OUKF) approach is proposed to elevate the adaptability and prediction performance of identification process. • ARMA noisy model is included in Kalman Filter (KF) to improve estimation precision and perturbations inhibiting performance. • The output of validations and identifications based on three KFs and ARMA model with sensitivity analysis will be presented. [ABSTRACT FROM AUTHOR]

The image-based visual servoing (IBVS) problem of a redundant underwater vehicle manipulator system (UVMS) with an eye-in-hand camera is investigated in this paper. With consideration of system constraints, dynamic uncertainties, and the absence of vehicle velocity sensors for a typical UVMS, we propose a hierarchical control architecture, which is composed of an unscented Kalman filtering (UKF)-based vehicle motion estimator, a kinematic model predictive IBVS controller, and two decoupled dynamic velocity controllers for the vehicle and the manipulator, respectively. In details, the underwater vehicle velocities are estimated by UKF with visual measurements based on the UVMS kinematic model. With these estimates, a nonlinear model predictive controller (MPC) is designed to solve the IBVS problem of UVMSs and generate velocity commands for the underwater vehicle and the manipulator, simultaneously. The visual kinematic model of UVMSs is used to predict the future trajectories, and the field-of-view, manipulator joint angle, and UVMS velocity constraints are handled when solving the optimization problem. To reduce the system complexity of UVMSs, the high-dimensional UVMS's dynamic model is decoupled into two low-dimensional models of the vehicle and the manipulator, and a dynamic inversion-based active disturbance rejection control (DI-ADRC) method is developed for dynamic controller design. The physical interaction effects between the vehicle and manipulator are considered as external disturbances acting on each other, which are estimated by extended state observers (ESO) in ADRC. The simulation experiments with a typical UVMS are performed to demonstrate the effectiveness of the proposed hierarchical IBVS controller in dealing with system redundancy, constraints and uncertainties. • A hierarchical MPC scheme integrated with ADRC is developed for IBVS of UVMSs. • The Decoupled dynamic controllers are proposed for the vehicle and the manipulator. • The vehicle velocities are estimated by UKF using visual measurements. • The Dynamic inversion-based ADRC control is proposed to deal with system uncertainties. [ABSTRACT FROM AUTHOR]

The paper designs a finite-time output feedback path following control strategy of underactuated marine surface vehicle (MSV) with the unavailable velocity, the unknown dynamic and unknown external environment disturbances. Firstly, a new finite time extended state observer (FTESO) is devised to accurately observe the unknown velocity and unknown lumped disturbances within finite time. Secondly, depending on the output of FTESO, the velocity observation-based finite-time LOS guidance approach is raised to acquire the reference yaw angle. Thirdly, the finite-time output feedback path following controllers are constructed through the backstepping method to track the desired path with precise tracking performance. It has been demonstrated that the observation error of FTESO is capable of converging to zero and the path following error is able to converge to an arbitrarily small domain near zero in finite time according to the homogeneous theory and the Lyapunov theory. Simulation comparison researches are performed to demonstrate the availability of the designed finite-time output feedback path following strategy. • The underactuated marine surface vehicle model is established and a finite-time output feedback path following control scheme based on the finite time extended state observer is designed. • The finite time extended state observer can estimate the unknown velocity and lumped disturbances, and the observation errors is proved to converge to zero in finite time by homogeneous theory. • The finite-time LOS guidance law based on the velocity observation can not only obtain the desired yaw angle but also meet the requirement of finite time. • The finite-time output feedback path following control scheme has faster tracking response, higher precision performance and stronger anti-interference ability. [ABSTRACT FROM AUTHOR]

This paper proposes a robust adaptive controller for the trajectory tracking of an autonomous surface vehicle (ASV) in the presence of model uncertainties, ocean currents, and time-varying environmental disturbances. First, a novel adaptive virtual velocity command is designed to estimate and compensate the unknown ocean currents acting on the ASV. Then, an improved torque controller is derived by applying filter operations and a set of auxiliary variables to avoid measuring the acceleration signals. To address the unknown dynamics parameters, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimated parameters and the tracking error can be achieved simultaneously. The whole closed-loop system's stability under environmental disturbances and parameter uncertainties is proved by the Lyapunov stability theory. Comparative simulations are carried out to validate the effectiveness of the proposed method. 1) In contrast to the existing results (Wang et al., 2017 , 2019b ; Van, 2019a , 2019b ; Shen et al., 2020b ; Qu et al., 2018 ; Huang and Yang, 2019 ; Cao and Lynch, 2016 ; Liu et al., 2020 ; Hou et al., 2020 ; Labbadi and Cherkaoui, 2020 ; Pan et al., 2013 , 2015a ; Li et al., 2016 ; Dehghani and Abedi, 2017 ; Zhang et al., 2020b) in which the ocean currents are not taken into account, a new update law that estimate and compensate the unknown ocean currents is proposed in this work in order to meet the marine reality. 2) Compared to (Belleter et al., 2019 ; Fossen and Lekkas, 2017 ; Pan et al., 2015b ; Wang et al., 2015 ; Caharija et al., 2014), the proposed algorithm can not only deal with constant current velocity, but also with time-varying current velocity. Several realistic aspects are also considered such as, parameter uncertainties induced by unmodeled dynamics and different operation conditions, unknown environmental disturbances due to wind, waves, etc. 3) Inspired by (Na et al., 2015 ; Mahyuddin et al., 2014 ; Luan et al., 2019), an adaptive control framework is proposed by applying a set of auxiliary filtered variables, such that the convergence of the tracking error and the estimated of the unknown dynamic parameters are achieved simultaneously. Moreover, the acceleration signal is not needed in the input control, which differs from the traditional gradient and recursive least square methods widely used in the existing adaptive controls (Boukattaya et al., 2012 , 2018). [ABSTRACT FROM AUTHOR]

Mobility and controllability of a deep ocean mining vehicle directly determine its operational efficiency and safety. In this study, the spatial hydrodynamic distributions acting on a mining vehicle under different motion states were ascertained through the computational fluid dynamics (CFD) numerical simulations. Then, a new multi-body dynamic (MBD) model of the vehicle integrated with its hydrodynamic effects and vehicle-sediment mechanics interaction was established. An underwater test of a tracked prototype vehicle in a large-scale laboratory water tank was performed and validated the feasibility and accuracy of the new MBD model. Furthermore, a controller based on a fuzzy self-adaptive PID method for the predetermined trajectory tracking of the mining vehicle was designed and modelled. Finally, the real-time information exchange and collaborative simulation between the developed MBD model and the controller were realized and proved that the proposed motion controller had a satisfactory performance in the trajectory tracking control for deep ocean mining vehicles. • A new multi-body dynamic model integrating hydrodynamics and sediment mechanics for a ocean mining vehicle was established. • An underwater test of a prototype vehicle in a water tank was performed and validated the accuracy of the new dynamic model. • A motion controller was designed and showed good performance in trajectory tracking control for the deepocean mining vehicle. [ABSTRACT FROM AUTHOR]

This paper focuses on motion controller design for x-rudder underwater vehicles with four independent stern rudders where the conventional ganging method for maneuvering is not applicable. To realize the cooperation of the stern rudders, motion controller of an x-rudder underwater vehicle is divided into two parts, a dynamics controller and a control allocator. The controller design is conducted under both scenarios that model parameters are known and unknown. For known models, the desired maneuvering moments are generated by model based sliding mode control method. For unknown models, non-model based iterative sliding mode control method is utilized to calculate virtual steering rudder commands. The control allocator of both methods are based on sequential quadratic programming to solve the mixed minimum problem, with varied evaluation criterions and constraints. By conducting numerical simulations under various conditions, the functionality of the two proposed control methods are verified, and comparison in terms of accuracy, energy saving, and computation consumption are also discussed in detail. • Motion control methods for x-rudder underwater vehicles are investigated. • A cascaded controller is proposed to decouple the over-actuated control problem. • Model-based sliding mode controller is proposed when domain hydrodynamic coefficients are known. • Iterative sliding mode based increment feedback control is proposed when domain hydrodynamic coefficients are unknown.. • Input saturation, rate limitation and energy optimization are considered in control allocation. [ABSTRACT FROM AUTHOR]

This paper analyses the transit performance of state-of-the-art underwater vehicles and presents an intermediate-fidelity steady-state flight mechanics model for qualitative performance assessment of underwater vehicles. Focusing on the comparison of underwater gliders and propeller-driven AUVs, a simple glide metric is presented and the transit performance of the legacy underwater gliders Slocum, Spray and Seaglider as well as propeller-modified versions thereof is evaluated. The evaluation is based on various data sets from wind tunnel tests and Computational Fluid Dynamics (CFD) studies, and shows that for the respective hull shapes gliding locomotion proves more efficient in ideal conditions. However, biofouling conditions inflict a double penalty on glider performance, rendering gliders inferior to propeller-driven vehicles. The Slocum data set is used to validate a steady-state flight mechanics model for qualitative performance prediction. It is shown that even simplistic models based on semi-empirical and analytical expressions can be successfully used for design optimization through parametrization. Being computationally efficient, the model can be a useful tool for design engineers in early design phases. The model is used to evaluate the effects of wing span on gliding efficiency, indicating that the current design of the Slocum glider is near-optimal. • Glide metric for underwater gliders is presented. • Transit performance of underwater gliders and propeller-driven vehicles is evaluated. • Underwater gliders are more efficient in ideal operating conditions. • Propeller-driven vehicles are more efficient under consideration of biofouling. • Transit performance can be estimated using intermediate-fidelity models. [ABSTRACT FROM AUTHOR]

In the paper, a robust controller using the back-stepping approach and the time delay estimation(TDE) is proposed for the trajectory tracking of torpedo-shape autonomous underwater vehicles(AUVs). The AUV has several difficult issues to design accurate tracking control: unsatisfaction of matching condition and highly nonlinear dynamics including external disturbances. To overcome the matching condition issues, the proposed controller is designed based on the back-stepping approach, which is a well-known method handling unmatched disturbances effectively and having well-established design procedure. To estimate nonlinear vehicle dynamics including sea currents and external disturbances, the TDE, an efficient estimation method using previous system information, is adopted. Owing simplicity of the TDE, the proposed controller does not need whole vehicle dynamics while requiring only the determination of control gains. The stability analysis of the proposed controller is studied, conducting the guidelines for selection of control gains. Through the simulation results using REMUS model, it is demonstrated that the proposed controller has accurate and robust tracking performance even when a vehicle moves under sea currents and external disturbing forces. • A robust tracking controller is proposed for torpedo-like underactuted AUVs. • The controller is robust against sea currents and external disturbing forces. • Not requring whole vehicle model, the controller is easy to be desiged. • The design guidelines based on the stability analysis are addressed. • The robustness of the controller is demonstrated through simulation with REMUS model. [ABSTRACT FROM AUTHOR]

This work describes an adaptive trajectory tracking controller for underactuated underwater vehicles. The control design process is two-fold; first, a high-level kinematic controller is designed to produce velocity commands that steer the vehicle towards the desired trajectory, and second, a low-level controller is designed to utilize the mathematical model of the vehicle to produce force and torque commands for the thrusters onboard the underwater vehicle. A novel adaptive Nussbaum-function-based controller is proposed and compared with an adaptive proportional controller with integral feedback. Further, a conditional adaptation scheme is developed to combat effects of noise, disturbance, or uncertainty. The proposed scheme governs adaptation such that the gains only adapt when it is necessary, even in the presence of noise. The target vehicle for the controller is a four-thruster quadcopter-like vehicle, which does not have direct control over all degrees of freedom. The issue of having an underactuated thruster arrangement is common in many classes of inspection robots. The devised adaptive control law exhibits fast convergence for the adaptation. Four different parametrized trajectories are tested, and the performance of the proposed algorithms, when considering the tracking errors and required control effort, is shown to be superior to an adaptive proportional controller. • Controller design for a novel quadcopter drone-like underwater vehicle. • Adaptive Nussbaum-function-based controller proposed. • Above controller compared with adaptive proportional controller. • Gain management scheme is developed to combat effects of noise/disturbances. [ABSTRACT FROM AUTHOR]