自主水下航行器的回坞导引和入坞控制算法

针对军事侦察和海洋环境监测领域中对自主水下航行器（AUV）水下自主回收能力的需求,研究了AUV自主回收过程中回坞和入坞的导引和控制问题。将水下自主回收过程分为回坞导引和入坞控制两个连续的阶段,其中回坞阶段采用经典的视线（LOS）导引法,使AUV到达回收器正前方的回坞航路点;入坞阶段则采用非线性横向跟踪控制方法,使AUV精确跟踪沿回收器中轴线的入坞直线航路航行并最终进入回收器。采用REMUS AUV的模型参数对水下回收进行了仿真研究,结果表明该方法是有效的,具有良好的工程应用前景。     

Depth control of autonomous underwater vehicles using indirect robust control method

In this article, we propose a robust depth control design scheme for autonomous underwater vehicles (AUVs) in the presence of hydrodynamic parameter uncertainties and disturbances. The controller is designed via a new indirect robust control method that handles the uncertainties by formulating the uncertainty bounds into the cost functional and then transforming the robust control problem into an equivalent optimal control problem. Both robust asymptotic stability and optimality can be achieved and proved with this new formulation. The θ-D method is utilised to solve the resultant nonlinear optimal control problem such that an approximate closed-form feedback controller can be obtained and thus is easy to implement onboard without intensive computation load. Simulation results demonstrate that robust depth control is accomplished under the system parameter uncertainties and disturbances with small control fin deflection requirement.     

A two‐step control approach for docking of autonomous underwater vehicles

This paper presents a novel integrated guidance and control strategy for docking of autonomous underwater vehicles. The approach to the base, and hence the control design, is divided in two steps: (i) in the first, at higher speed, the vehicle dynamics is assumed to be underactuated, and an appropriate control law is derived to steer the vehicle towards the final docking path, achieving convergence to zero of the appropriate error variables for almost all initial conditions; (ii) in the second stage, at low speed, the vehicle is assumed to be fully actuated, and a robust control law is designed that achieves convergence to zero of the appropriate error variables for all initial conditions, in the presence of parametric model uncertainty. Simulations are presented illustrating the performance of the proposed controllers, including model uncertainty and sensor noise. Copyright © 2014 John Wiley & Sons, Ltd.     

A time differences of arrival‐based homing strategy for autonomous underwater vehicles

A new sensor‐based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in 3‐D, using the information provided by an ultra‐short baseline (USBL) positioning system. The guidance and control law is first derived at a kinematic level, expressed on the space of the time differences of arrival (TDOAs), as directly measured by the USBL sensor, and assuming the plane wave approximation. Afterwards, the control law is extended for the dynamics of an underactuated AUV resorting to backstepping techniques. The proposed Lyapunov‐based control law yields almost global asymptotic stability (AGAS) in the absence of external disturbances and is further extended, keeping the same properties, to the case where known ocean currents affect the motion of the vehicle. Simulations are presented and discussed that illustrate the performance and behavior of the overall closed‐loop system in the presence of realistic sensor measurements and actuator saturation. Copyright © 2009 John Wiley & Sons, Ltd.     

水下机器人协同控制的TO模型区域划分定位

针对稀疏型水声传感器网络定位算法面临的定位覆盖率低和误差高的问题, 本文提出一种水下机器人协 同控制的截角八面体(TO)模型区域划分定位算法. 首先搭建定位系统模型, 提出TO模型满足三维目标区域划分原 则, 并证明其体积比相对最优; 然后设计TO模型最优区域划分方式, 提出最小值判定法进一步整合目标节点, 自主 水下机器人(AUVs)协同控制筛选包含目标节点的子区域; 通过分析通信半径和虚拟锚节点数量对实验结果的影响, 设置最优定位参数, 降低能耗和定位误差, 最后利用最小二乘法完成定位. 本文分别对定位覆盖率、子区域AUV路 径长度和定位精度进行了仿真实验, 结果表明, 相比于其他区域划分方案, 所提算法误差较小、定位覆盖率高且鲁 棒性强.     

生物启发神经动力学模型的自治水下机器人反步跟踪控制

本文提出一种生物启发神经动力学模型的自治水下机器人(autonomous underwater vehicles,AUV)三维轨迹跟踪控制算法.将AUV在三维空间的运动分为水平面运动和垂直面运动,针对传统反步轨迹跟踪控制器出现的速度跳变问题,采用生物启发神经动力学模型,通过构造一种简单的中间虚拟变量,同时结合Lyapunov函数设计出轨迹跟踪控制律,使其控制效果在水平面和垂直面都能够达到全局渐近稳定并且有平滑连续的输出结果,克服AUV反步轨迹跟踪控制的速度跳变问题.仿真结果证明了所提控制律的有效性.     

考虑通信延迟的多自治水下航行器协同定位算法

自治水下航行器(AUV)协同定位中通信延迟具有常态性. 面对延迟到达的信息, 传统方法一般会有定位精 度或实时性的损失. 针对通信延迟的不利影响, 本文在建立水声探测和通信时延模型的基础上, 以扩展卡尔曼滤波 (EKF)为算法框架, 提出了信息顺序到达和信息出序到达2种协同定位算法, 并以建构面向信息出序情景的算法为 主要创新工作. 在信息顺序到达算法中, 将延迟信息进行序贯处理以减小定位误差. 在信息出序到达算法中, 以信 息出现一步滞后的延迟为背景, 使用出序信息直接对从AUV最新状态估计进行再更新, 信息无损地实时估计运动 状态. 计算机仿真实验结果表明, 本文算法相比于传统的航位推算、整周期滤波、量测丢弃等方法, 具有更高的估计 精度; 相比于数据缓存滤波、重新滤波等方法, 具有强实时性.     

不确定海流环境下水下机器人最优时间路径规划

为解决海流预测不精确条件下,现有基于确定性海流路径规划算法鲁棒性差和规划的路径有可能为不可行路径的问题,本文提出一种基于区间优化的水下机器人(AUV)最优时间路径规划算法.该算法采用双层架构,外层用蚁群系统算法(ACS)寻找由起点至终点的候选路径;内层以区间海流为环境模型,计算候选路径航行时间上下限,并分别通过区间序关系和基于可靠性的区间可能度模型将航行时间区间转换为确定性评价函数,并将评价函数值作为候选路径适应度值返回到外层算法.仿真结果表明,相对于确定海流场路径规划方案,提出的方案增强了路径规划器的鲁棒性并解决了结果路径不可行问题.     

Synchronization of multiple autonomous underwater vehicles without velocity measurements

In this paper,we investigate the synchronization control of multiple autonomous underwater vehicles (AUVs),considering both state feedback and output feedback cases.Treating multiple AUVs as a graph,we define the tracking error of each AUV with both its own tracking error and the relative position errors with respect to its neighbors taken into account.Lyapunov analysis is used to derive the control law for each AUV.For the output feedback case,a passive filter is used to compensate for the unknown relative velocity errors among AUVs,and an observer is employed to estimate the velocity of the AUV itself.Rigid mathematical proof is provided for the proposed algorithms for both state feedback and output feedback cases.Simulations are provided to demonstrate the effectiveness of the proposed approach.It is shown that,the synchronization error is smaller in the case of considering the relative errors between AUVs than in the case of considering the tracking error of the single AUV only.     

利用FastSLAM框架的多自治水下航行器同时定位与跟踪算法

协同定位是共融机器人研究领域的重要问题.协同定位方案的制定受限于机器人间信息交互的能力.针对长时间通讯中断时多自治水下航行器(AUV)协同定位精度明显下降的问题,借鉴同时定位与制图(SLAM)方法,提出了基于FastSLAM框架的同时定位与跟踪(SLAT)算法.将主AUV视为非合作目标,在从AUV上建立起一个关于主AUV的运动估计器,利用从AUV上声呐传感器实时获取的相对量测信息,在对主AUV运动状态估计的同时,完成对从AUV自定位精度的提升.仿真实验结果表明,在长时间通讯中断发生的条件约束下,相比于传统的航位推算方法,所提出的SLATF1.0和2.0算法能够有效减小定位误差,2.0算法对于探测精度变化等因素的影响具有更好适应性.     

基于神经网络的欠驱动水下机器人三维同步跟踪和镇定控制

研究了欠驱动水下机器人的三维同步跟踪和镇定控制问题,并考虑了模型参数不确定性、未知外界干扰和输入饱和限制的影响.针对不同类型期望轨迹的特性,构造了新的辅助虚拟信号以实现对欠驱动方向的控制.基于反步法和Lyapunov直接法,设计了一种饱和自适应统一动力学控制律,使得AUV的状态误差最终收敛至零点附近的有界区域内,其中未...     

Autonomous underwater vehicles: Hybrid control of mission and motion

This paper provides an experimental implementation and verification of a hybrid (mixed discrete state/ continuous state) controller for semi-autonomous and autonomous underwater vehicles in which the missions imply multiple task robot behavior. An overview of some of the missions being considered for this rapidly developing technology is mentioned including environmental monitoring, underwater inspection, geological survey as well as military missions in mine countermeasures.The functionalities required of such vehicles and their relation to intelligent control technology is discussed. In particular, the use of Prolog as a computer language for the specification of the discrete event system (DES) aspects of the mission control is proposed. The connections between a Prolog specification and the more common Petri Net graphical representation of a DES are made. Links are made between activation commands, transitioning signals, and the continuous state dynamic control system (DCS) responsible for vehicle stabilization.Details are given of the NPS Phoenix vehicle implementation at the present time, together with experimental validation of the concepts outlined using a simplified example mission. The paper ends with a listing of questions and concerns for the evaluation of software controllers. A list of references is given for readers interested in this subject.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Seafloor map generation for autonomous underwater vehicle navigation

Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.     

一种水下机器人运动的过程神经元控制

过程神经网络是传统神经网络的拓展, 增加了一个对于时间的聚合算子, 从而更好地模拟了生物神经元的信息处理机制. 这是由于水下机器人运动控制系统的输入、输出均是随时间连续变化的过程量. 结合S函数和预先规划思想, 建立水下机器人过程神经元的运动控制模型. 仿真试验证明,该新型控制模型, 对于水下机器人的运动非线性控制器具有设计简单、响应速度快、超调小、鲁棒性好等优点.     

基于卡尔曼滤波的自主式水下航行器大尺度编队控制

针对网络环境下环境噪声对自主式水下航行器编队控制的影响,提出一种利用卡尔曼滤波实时估计AUV最优运动状态的编队控制方法.将空间间隔较远的多AUV系统建模为多智能体系统,从大尺度上研究其编队控制问题.为了得到每个AUV速度状态的最优估计值,每个AUV都嵌入一个全局卡尔曼滤波器,利用该全局滤波器进行最优估计从而计算出噪声环境下其自身的最优位置.仿真结果验证了所给出的控制策略的有效性.     

采用一致性算法与虚拟结构的多自主水下航行器编队控制

采用一致性算法与虚拟结构法研究了多自主水下航行器(AUV)小尺度编队控制问题.首先针对各自主水下航行器拥有不同虚拟领航者信息(参考信息)的情况,通过对各AUV拥有的不一致参考信息进行一致性协商而达到状态一致.其次,基于虚拟结构思想采用坐标变换将各AUV相对于虚拟领航者的相对位置转换为各自的期望位置,并设计了一种有限时间跟踪控制律以确保各AUV能在有限时间内跟踪上其期望轨迹,从而实现了多AUV的小尺度有限时间编队控制.最后仿真实验验证了控制策略的有效性.     

欠驱动水下机器人三维轨迹跟踪有限时间预设性能控制

本文研究了具有不确定动态和未知时变海洋环境扰动的欠驱动水下机器人(AUVs)三维轨迹跟踪有限时间预设性能控制问题,提出新型预设性能函数和误差映射函数,将受预设性能限制的轨迹跟踪误差转变为非受限的变换后误差;构造新的超螺旋(ST)扩张状态观测器,在有限时间内实时估计AUV不确定动态和未知时变海洋环境扰动引起的总扰动;基于...