Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean currents.An active sensor fault-tolerant control scheme is proposed.First,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor faults.Subsequently,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback method.It is shown that the effect of sensor faults is online estimated and compensated by an adaptive estimator.With the proposed controller,the tracking error and estimation error can asymptotically converge to zero.Finally,simulation results are performed to demonstrate the effectiveness of the proposed method.

Technology Development of Unmanned Underwater Vehicles (UUVs)

In recent years, the weapon systems have been changing drastically because of the advancement of science technology and the change of military concept of combat. There is an unmanned system at the center of all those changes. Especially, in case of maritime environment, as the center stage of combat has changed from ocean to coastal areas, it is difficult for the existing naval forces to effectively operate in shallow waters. Therefore, unmanned underwater vehicles (UUVs) are being required at an increasing pace. In this paper, we analyze the characteristics of already developed UUVs, which are the key unmanned system of the marine battlefield environment in the future. Through the analysis of development cases and the investigation of the essential technologies, the critical design issues of UUVs are elaborated. We also suggest the future directions of the UUV technologies based on the case analysis.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Application of unmanned underwater vehicles in polar research

The importance of polar ice as vital components of the global ocean-climate system is widely recognized.In this paper,we demonstrate the importance and urgency of polar research,describe the primary characteristics of sea ice and ice shelves,and outline the current status and difficulties associated with sub-ice research.We highlight the importance of Unmanned Underwater Vehicles(UUVs)as important tools for oceanographic research.We present recent progress in UUV deployment in sub-ice research in the Arctic and the Antarctic,and review the latest international developments in UUV structure,navigation,payload,and field operation.Moreover,Chinese polar UUVs and their deployments in the polar regions are presented in detail.Key technologies and solutions regarding polar application of UUVs(e.g.,sub-ice navigation and positioning,energy supply and data transmission,and sub-ice guidance and recovery)are discussed.Given the current worldwide attention on polar science,the potential future directions of UUV-related polar research(e.g.,observations under Antarctic ice shelves,long-range surveys beneath Arctic sea ice and application of intelligent technology)are discussed.

Using Unmanned Underwater Vehicles as Research Platforms in Coastal Ocean Studies

The advantages of using unmanned underwater vehicles in coastal ocean studies are emphasized. Two types of representative vehicles, remotely operated vehicle (ROV) and autonomous underwater vehicle (AUV) from University of South Florida, are discussed. Two individual modular sensor packages designed and tested for these platforms and field measurement results are also presented. The bottom classification and albedo package, BCAP, provides fast and accurate estimates of bottom albedos, along with other parameters such as in-water remote sensing reflectance. The real-time ocean bottom optical topographer, ROBOT, reveals high-resolution 3-dimentional bottom topography for target identification. Field data and results from recent Coastal Benthic Optical Properties field campaign, 1999 and 2000, are presented. Advantages and limitations of these vehicles and applications of modular sensor packages are compared and discussed.

Experimental Study of a Modified Command Governor Adaptive Controller for Depth Control of an Unmanned Underwater Vehicle

Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.

Multiple Unmanned Underwater Vehicles Consensus Control with Unmeasurable Velocity Information and Environmental Disturbances Under Switching Directed Topologies

A consensus algorithm proposed in the paper is applied to tackle remarkable problems of unmeasurable velocities,the environmental disturbances, and the limited communication environment for the multiple unmanned underwater vehicles(multi-UUVs). Firstly, for a complex nonlinear and coupled model of the unmanned underwater vehicle(UUV), a technique of feedback linearization is developed to transform the nonlinear UUV model into a secondorder integral UUV model. Secondly, to address the problem of the unavailable velocity information and environmental disturbances for the multi-UUVs system, we design a distributed extended state observer(DESO) to estimate the unmeasurable velocities and environmental disturbances using the relative position information. Finally,we propose a protocol based on the estimation information from the DESO and demonstrate that the multi-UUVs system with the switching directed topologies under the protocol can reach consensus asymptotically. The theoretical result proposed in the literature is verified by one numerical example.

Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial institutions.One of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine attacks.This study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity strategy.The Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity solution.The research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical information.The results suggest that detecting the submarine early increases the likelihood of averting a collision.The dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access efficiency.Securing communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the task.The swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its peers.This,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other UAVs.The dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

Attitude Stabilization of Unmanned Underwater Vehicle During Payloads Release

Large unmanned underwater vehicles can carry big payloads for varied missions and it is desirable for them to possess an upright orientation during payload release.Their attitude can hardly be maintained during and after the phase of payload release.Releasing a payload from the vehicle induces uncertainties not only in rigid-body parameters,e.g,the moment of inertia tensor due to the varying distribution of the masses on board the vehicle,but also in the hydrodynamic derivatives due to the vehicle’s varying geometric profile.A nonlinear attitude stabilizer that is robust to these time-varying model uncertainties is proposed in this paper.Stability is guaranteed via Lyapunov stability theory.The simulation results verify the effectiveness of the proposed approach.

Hydrodynamic Modeling with Grey-Box Method of A Foil-Like Underwater Vehicle

In this study, a dynamic modeling method for foil-like underwater vehicles is introduced and experimentally verified in different sea tests of the Hadal ARV. The dumping force of a foil-like underwater vehicle is sensitive to swing motion. Some foil-like underwater vehicles swing periodically when performing a free-fall dive task in experiments. Models using conventional modeling methods yield solutions with asymptotic stability, which cannot simulate the self-sustained swing motion. By improving the ridge regression optimization algorithm, a grey-box modeling method based on 378 viscous drag coefficients using the Taylor series expansion is proposed in this study. The method is optimized for over-fitting and convergence problems caused by large parameter matrices. Instead of the PMM test data, the unsteady computational fluid dynamics calculation results are used in modeling. The obtained model can better simulate the swing motion of the underwater vehicle. Simulation and experimental results show a good consistency in free-fall tests during sea trials, as well as a prediction of the dive speed in the swing state.

A new robust fuzzy method for unmanned flying vehicle control

A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.

基于BP神经网络的非线性流域UUV动态回收过程预测

针对水下无人自主航行器(UUV)回收过程中流域存在非线性干扰问题,提出了一种基于BP神经网络优化UUV回收路径的闭环控制方法。采用计算流体力学(CFD)方法模拟UUV相对于潜艇以不同路径进行回收的水动力系数,将数值模拟结果作为训练BP神经网络的初始数据,利用拉丁超立方法对非线性流域的位置随机采样,采用神经网络输出UUV在采样处的水动力系数,实现非线性流域内UUV动态回收过程的水动力系数预测。结果表明:通过均方根检验神经网络预测水动力系数误差均在10%范围内。将神经网络预测结果与UUV纵向操纵性方程结合,对比回收速度和操舵间隔与理论回收轨迹的误差,优化UUV动态回收路径的闭环控制方案。

近海复杂环境下UUV动态路径规划方法研究

为解决近海环境下水下无人航行器(unmanned underwater vehicle,UUV)的动态路径规划问题,本文提出一种结合全局和局部动态路径规划的算法。首先,本文提出一种基于自适应目标引导的快速拓展随机树算法,以增加随机树生长的方向性,并通过转向和重选策略减少无效拓展加快算法的收敛速度。接着,获得全局路径之后使用自适应子节点选取策略获取动态窗口法的子目标点,将复杂的全局动态任务规划分解为多个简单的动态路劲规划,从而防止动态窗口法陷入局部极小值。最后,通过UUV出港任务仿真实验验证了算法的有效性和实用性。

海水对UUV WPT系统的影响及耦合机构的补偿研究

针对水下无人航行器(UUV)无线电能传输(WPT)系统的效率和稳定性问题,建立了水下WPT等效电路,并对其工作原理进行了数学推导和分析。以此为基础研究了海水环境对WPT电路性能的影响机理,推导出了WPT系统在海水中的传输功率及效率模型。由于在海浪和海流等水下外界干扰下电能传输机构的收/发端均会产生偏移,从而影响系统电能传输的稳定性和效率。为减小影响,分析了4种典型拓扑补偿电路结构,并给出了最优补偿电路选取原则。最后,在MATLAB平台进行验证,结果验证了数学推导的正确性以及补偿电路设计的有效性。研究结果可为类似水下无线供电系统的设计提供借鉴。

不同间距和航速下水下航行体近场运动UUV受力分析

为探究不同侧向间距和航速下水下航行体绕流流场对UUV的流体动力干扰规律,以实尺度水下航行体和UUV模型为研究对象,通过数值求解RANS方程,基于重叠网格方法进行水下航行体近场运动UUV的非定常和定常数值模拟研究。首先,简要分析不同侧向间距和航速下UUV作匀速直线运动时轴向力、侧向力和偏航力矩系数随轴向位置的变化规律,在此基础上进一步探讨侧向间距和航速对UUV受力规律的影响并分析二者影响作用的大小。结果表明,对于轴向力,航速和侧向间距均表现出明显的影响,航速的影响作用要略大于侧向间距;而对于侧向力和偏航力矩,侧向间距的影响要远大于航速,航速的影响作用基本可以忽略。

声光联合引导UUV回收系统光学子系统研究

当今水下无人航行器(UUV)在军事与民用领域都有着广泛的应用前景,布放与回收技术是影响UUV实际应用的一项关键技术。目前主要回收引导方式有声学导引、光学导引、电磁导引这3种,而各自都存在明显的优缺点。本文针对声光联合导引机械臂回收系统光学子系统进行研究,设计一组可在水下200 m、0.05~10 m范围工作的机器视觉镜头及其配合使用的靶标,并完成了系统的仿真。该系统可在环境要求下准确地提取出靶标特征点。

基于固定时间积分滑模观测器的双闭环UUV轨迹跟踪控制

针对水下无人航行器(UUV)轨迹跟踪控制问题,设计一种基于固定时间积分滑模观测器(FTISO)的双闭环运动控制策略。建立UUV运动数学模型,对运动学外环设计积分滑模控制器,使用非线性微分器获得外环虚拟控制律的导数,以保证航行器位置能够精确收敛。考虑动力学内环存在的模型不确定性和外部干扰难以测量等问题,采用FTISO实现对集总干扰快速估计,同时引入李雅普诺夫函数分析系统的稳定性。在仿真环境下对比基于FTISO的双闭环积分滑模控制与积分滑模控制、RBF网络自适应滑模控制的控制效果。仿真结果表明:基于FTISO的双闭环滑模控制对集总干扰有良好的补偿能力,可提高控制系统的性能及精度,可保证UUV在不同环境下均能实现轨迹跟踪。

基于机器视觉的UUV水下沉底目标自主识别

水下无人航行器对目标的自动识别研究是未来水下目标探测领域的发展趋势。针对现阶段水下沉底目标声呐图像探测人工识别工作量大、判别困难等问题,研究了机器视觉中基于Canny算子图像边缘检测结合霍夫线变换去噪的识别方法在水下无人航行器自主探测的应用,为此在湖底布放多个水下沉底模拟目标,尝试了水下无人航行器的目标自主探测方案,通过实际湖上试验对比分析三种水下沉底目标声呐图像的算法识别效果,验证了论文所研究方法的有效性,为后续无人平台自主识别算法研究提供参考,以期提高无人航行器对水下沉底目标的自主探测识别能力。

基于IGWO-MPC和NDO-ISMC级联算法的UUV轨迹跟踪控制器设计

为了解决水下机器人(UUV)在受海流等复杂干扰力影响时的轨迹跟踪问题,设计了一种基于改进灰狼算法的模型预测控制(IGWO-MPC)和基于非线性干扰观测器的积分滑模控制(NDO-ISMC)级联的轨迹跟踪控制器。基于MPC方法设计了运动学控制器,提出利用在GWO算法中引入非线性收敛因子,基于个体经验和权重更新灰狼位置,以及反向学习策略所设计出的IGWO算法,来优化MPC的滚动优化求解过程,使机器人以期望的最优速度对给定位姿进行跟踪。基于IGWO-MPC运动学控制器得到的期望速度指令,设计了NDO-ISMC动力学控制器,该控制器利用NDO对干扰力进行估计,并对ISMC动力学输出控制律进行补偿以抑制扰动对跟踪精度的影响,同时输出扰动条件下机器人能够稳定跟踪上期望速度的控制推力。最后,利用MATLAB进行三维空间环境仿真,验证文章提出的将IGWO-MPC与NDO-ISMC算法级联的轨迹跟踪控制器能够使UUV在复杂水下环境下实现精确且稳定的轨迹跟踪。

基于DoDAF和SysML的潜艇与UUV协同作战概念描述方法

随着UUV等水下无人装备及技术的不断发展,水下无人装备在作战领域逐渐深入,探索潜艇与UUV协同的作战概念对于牵引水下装备发展具有重要意义。基于DoDAF(Department of Defense Architecture Framework)框架和SysML语言(Unified Modeling Language),建立潜艇与UUV协同作战概念描述方法,对潜艇与UUV协同反舰的作战概念进行了顶层、规范化的描述,可为潜艇与UUV协同作战概念研究提供参考。