深海AUV无动力垂直下潜运动特性研究

深海AUV(自主水下机器人)采用无动力下潜方式能有效节约能源,但是水平或小纵倾角姿态下潜用时较长,水平偏移大。为提高下潜速度,减小下潜偏移距离,对AUV无动力垂直下潜过程进行研究。使用四元数代替欧拉角得到AUV运动方程,并加入洋流和浮力数学模型,通过Simulink仿真模块建立AUV垂直下潜运动仿真方法,解决垂直下潜产生的姿态角解算奇异性问题。通过设置不同仿真工况,得到AUV垂直下潜运动状态量受负浮力、重浮心距离、舵角、初始纵倾角及环境扰动力的作用规律。结果表明,AUV采用垂直下潜方式,可实现较大的下潜速度,同时减小水平偏移距离;洋流扰动是垂直下潜运动产生水平偏移的主要原因;下潜深度6000 m时,浮力变化扰动导致末端下潜速度减小30%,下潜时间增加21%。

面向海流扰动和通信时延的欠驱动AUV编队跟踪控制

由欠驱动自主水下航行器(Autonomous Underwater Vehicle,AUV)编队组成的水下探测阵列具有响应快、机动性好、智能化程度高的特点。针对无速度信息传输的多AUV在海流和时延复合扰动下的编队控制问题,提出一种虚拟轨迹、轨迹预测、自适应反演滑模控制技术相结合的编队控制方法。使用领航者的位置信息和期望队形得到跟随者的参考轨迹,并引入虚拟轨迹使其与跟随者参考轨迹有限时间重合,从而得到跟随者的期望位置和速度。在此基础上,使用最小二乘法拟合轨迹曲线进行时延补偿,并通过自适应反演滑模控制技术完成海流干扰下的编队轨迹跟踪。理论分析和仿真结果表明,新方法具有可行性和有效性。

Consensus of multiple autonomous underwater vehicles with double independent Markovian switching topologies and timevarying delays

A new method in which the consensus algorithm is used to solve the coordinate control problems of leaderless multiple autonomous underwater vehicles(multi-AUVs) with double independent Markovian switching communication topologies and time-varying delays among the underwater sensors is investigated.This is accomplished by first dividing the communication topology into two different switching parts,i.e.,velocity and position,to reduce the data capacity per data package sent between the multi-AUVs in the ocean.Then,the state feedback linearization is used to simplify and rewrite the complex nonlinear and coupled mathematical model of the AUVs into a double-integrator dynamic model.Consequently,coordinate control of the multi-AUVs is regarded as an approximating consensus problem with various time-varying delays and velocity and position topologies.Considering these factors,sufficient conditions of consensus control are proposed and analyzed and the stability of the multi-AUVs is proven by Lyapunov-Krasovskii theorem.Finally,simulation results that validate the theoretical results are presented.

一种AUV模块化载体结构设计与实现

随着科技的不断发展,水下作业任务呈多样化、复杂化的趋势,针对自主式水下无人航行器功能多样性的问题,采用模块化的设计思想,提出一种载体结构设计方法。以某300 m级探测型AUV为例,对本文提出的载体结构进行验证。水池试验结果表明,本文提出的模块化载体结构运行情况良好,保证了AUV各项功能的实现,可为水下无人航行器的工程设计提供参考。

Tracking Control of Autonomous Underwater Vehicles with Internal Moving Mass

追踪轨道的控制问题被调查为一自治在水下作为致动器用一个内部点团和一个后面的推进器搬到垂直飞机的车辆(AUV ) 。与点团的动力学结合了， AUV 作为一个 underactuated 系统被建模。一个基于 Lyapunov 的追踪控制器被使用 backstepping 途径稳定错误动力学并且强迫位置错误到起源的一位小邻居建议。模拟结果验证建议追踪的途径。

Analysis and innovation of key technologies for autonomous underwater vehicles

As the mission needs of the autonomous underwater vehicles(AUV) have become increasingly varied and complex,the AUVs are developing in the direction of systematism, multifunction, and clustering technology, which promotes the progress of key technologies and proposes a series of technical problems. Therefore, it is necessary to make systemic analysis and in-depth study for the progress of AUV's key technologies and innovative applications. The multi-functional mission needs and its key technologies involved in complex sea conditions are pointed out through analyzing the domestic and foreign technical programs, functional characteristics and future development plans. Furthermore, the overall design of a multi-moving state AUV is proposed. Then, technical innovations of the key technologies, such as thrust vector, propeller design, kinematics and dynamics, navigation control, and ambient flow field characteristics, are made, combining with the structural characteristics and motion characteristics of the new multi-moving state AUV. The results verify the good performance of the multi-moving state AUV and provide a theoretical guidance and technical support for the design of new AUV in real complex sea conditions.

Innovative design and motion mechanism analysis for a multi-moving state autonomous underwater vehicles

In order to achieve the functional requirements of multi-moving state, a new autonomous underwater vehicle(AUV) provided with the functions such as the submarine vectorial thrust, landing on the sea bottom, wheel driving on the ground and crawling on the ground was designed. Then five new theories and methods were proposed about the motion mechanism of the AUV such as vectorial thruster technology, design of a new wheel propeller, kinematics and dynamics, navigation control and the ambient flow field in complex sea conditions, which can all conquer conventional technique shortages and predict the multi-moving state performance under wave disturbance. The theoretical research can realize the results such as a vectorial transmission shaft with the characteristics of spatial deflexion and continual circumgyratetion, parameterized design of the new wheel propeller with preferable open-water performance and intensity characteristics satisfying multi-moving state requirements, motion computation and kinetic analysis of AUV's arbitrary postures under wave disturbance, a second-order sliding mode controller with double-loop structure based on dynamic boundary layer that ensures AUV's trajectory high-precision tracking performance under wave disturbance, fast and exact prediction of the ambient flow field characteristics and the interaction mechanism between AUV hull and wheel propellers. The elaborate data obtained from the theoretical research can provide an important theoretical guidance and technical support for the manufacture of experimental prototype.

Dynamics Modeling and Simulation of Autonomous Underwater Vehicles with Appendages

为设计并且调试提供一个模拟系统平台一小自治在水下车辆(AUV ) 运动控制器，一自由(6-DOF ) 六度为推进器和鳍与附器控制的 AUV 的动态模型被检查。基于动态模型，为 AUV 运动的一个模拟系统被建立。典型运动的不同类型被模仿分析运动表演和 AUV 的可操作性。以便在 AUV 的运动表演上评估附器的影响，有或没有附器的 AUV 的模拟被执行并且比较。结果证明 AUV 有或没有附器有好可操作性。

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller (PNNC) is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV-IV" and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller’s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

A nonlinear bottom-following controller for underactuated autonomous underwater vehicles

The bottom-following problem for underactuated autonomous underwater vehicles(AUV) was addressed by a new type of nonlinear decoupling control law.The vertical bottom-following error and pitch angle error are stabilized by means of the stern plane,and the thruster is left to stabilize the longitudinal bottom-following error and forward speed.In order to better meet the need of engineering applications,working characteristics of the actuators were sufficiently considered to design the proposed controller.Different from the traditional method,the methodology used to solve the problem is generated by AUV model without a reference orientation,and it deals explicitly with vehicle dynamics and the geometric characteristics of the desired tracking bottom curve.The estimation of systemic uncertainties and disturbances and the pitch velocity PE(persistent excitation) conditions are not required.The stability analysis is given by Lyapunov theorem.Simulation results of a full nonlinear hydrodynamic AUV model are provided to validate the effectiveness and robustness of the proposed controller.

Cooperative Navigation for Autonomous Underwater Vehicles Based on Estimation of Motion Radius Vectors

A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acoustic communication network among the group members, the relative positioning problem can be solved. A novel approach for solving the relative positioning is presented by using a recursive trigonometry technique and extended Kalman filter(EKF). Simulation results verify the correctness and effectiveness of this navigation method.

Numerical computation and analysis of high-speed autonomous underwater vehicle (AUV) moving in head sea based on dynamic mesh

Autonomous underwater vehicles (AUVs) navigating on the sea surface are usually required to complete the communication tasks in complex sea conditions.The movement forms and flow field characteristics of a multi-moving state AUV navigating in head sea at high speed were studied.The mathematical model on longitudinal motion of the high-speed AUV in head sea was established with considering the hydrodynamic lift based on strip theory,which was solved to get the heave and pitch of the AUV by Gaussian elimination method.Based on this,computational fluid dynamics (CFD) method was used to establish the mathematical model of the unsteady viscous flow around the AUV with considering free surface effort by using the Reynolds-averaged Navier-Stokes (RANS) equations,shear-stress transport (SST) k-w model and volume of fluid (VOF) model.The three-dimensional numerical wave in the computational field was realized through defining the unsteady inlet boundary condition.The motion forms of the AUV navigating in head sea at high speed were carried out by the program source code of user-defined function (UDF) based on dynamic mesh.The hydrodynamic parameters of the AUV such as drag,lift,pitch torque,velocity,pressure,and wave profile were got,which reflect well the real ambient flow field of the AUV navigating in head sea at high speed.The computational wave profile agrees well with the experimental phenomenon of a wave-piercing surface vehicle.The force law of the AUV under the impacts of waves was analyzed qualitatively and quantitatively,which provides an effective theoretical guidance and technical support for the dynamics research and shape design of the AUV in real complex environment.

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

在这份报纸，一在水下车辆与运动的六个维的非线性的方程被建模，在自由的所有度由 DC 马达控制了。在精力充沛的环境为的在最佳附近的轨道在水下车辆用一个非线性的最佳的控制问题(NOCP ) 的一个数字答案被计算。一个费用函数，应该被最小化，被定义的一个精力性能索引。产生问题是一个二点的边界价值问题(TPBVP ) 。一个基因算法(GA ) ，粒子群优化(PSO ) ，和蚂蚁殖民地优化(ACO ) 算法被使用解决产生 TPBVP。把一个 Euler-Lagrange 方程用于 NOCP，一个 conjugate 坡度惩罚方法也被采用解决 TPBVP。精力充沛的环境的问题，包含一些精力采购原料，被讨论。一些在最佳附近的路径用 GA， PSO，和 ACO 算法被发现。最后，在精力充沛的环境的碰撞回避的问题也被考虑。

基于CFD的具有首翼的新型飞翔形式AUV升力和阻力估计:对阻力极曲线和推力估计的见解

To achieve hydrodynamic design excellence in Autonomous Underwater Vehicles(AUVs)largely depends on the accurate prediction of lift and drag forces.The study presents Computational Fluid Dynamics(CFD)-based lift and drag estimations of a novel torpedo-shaped flight-style AUV with bow-wings.The horizontal bow-wings are provided to accommodate the electromagnetic arrays used to perform the cable detection and tracking operations near the seabed.The hydrodynamic performance of the AUV due to addition of these horizontal bow-wings is required to be investigated,particularly at the initial design stage.Hence,CFD techniques are employed to compute the lift and drag forces observed by the flight-style AUV,maneuvering underwater at different angles of attack and varying speeds.The Reynolds-Averaged Navier-Stokes Equations(RANSE)closure is achieved by employing the modified k-ϵ model and Two-Scale Wall Function(2-SWF)approach is used for boundary layer treatment.Further,the study also highlights the unique mesh refinement and solution-adaptive meshing techniques to perform the CFD simulations in Solidworks Flow Simulation(SWFS)environment.The drag polar curve for flight-style AUV with and without bow-wings is generated using the computed lift and drag coefficients.The curve provided essential insights for achieving hydrodynamically efficient and optimized AUV design.From the drag polar curve,it is revealed that due to horizontal bow-wings,the flight-style AUV is capable to generate higher lift with less drag and thus,it gives better lift-to-drag ratio compared to the AUV without bow-wings.Moreover,simulated results of axial drag observed by the AUV have also been compared with free-running experimental results and are found in good agreement.

基于高阶递归神经网络的AUV鲁棒控制方法

目的 提出一种基于高阶递归神经网络的AUV鲁棒控制方法。方法 利用结构简单但逼近效果优越的高阶递归神经网络,对建模不确定性和外部未知干扰进行估计,并将其补偿到输入控制律中,以提高控制性能。之后,基于HJI理论和Lyapunov稳定性分析导出神经网络权重自适应更新律和AUV自适应控制律,设计反步滑模方法作为对比方法,并进行仿真实验。结果 设计的基于高阶递归神经网络的AUV鲁棒控制方法的跟踪误差、调节时间等控制指标均优于反步滑模方法。设计的鲁棒控制方法可以控制AUV精确跟踪目标轨迹,同时具有优秀的控制性能和鲁棒性。结论 这一研究为AUV轨迹跟踪控制领域提供了一种高效且有效的方法,有望在复杂、不确定的水下环境中得到应用。

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Expert S-surface control for autonomous underwater vehicles

S-surface control has proven to be an effective means for motion control of underwater autonomous vehicles(AUV).However there are still problems maintaining steady precision of course due to the constant need to adjust parameters,especially where there are disturbing currents.Thus an intelligent integral was introduced to improve precision.An expert S-surface control was developed to tune the parameters on-line,based on the expert system,it provides S-surface control according to practical experience and control knowledge.To prevent control output over-compensation,a fuzzy neural network was included to adjust the production rules to the knowledge base.Experiments were conducted on an AUV simulation platform,and the results show that the expert S-surface controller performs better than an S-surface controller in environments with currents,producing good steady precision of course in a robust way.

基于大脑海马认知机理的主从式AUV协同定位方法

针对AUV协同定位系统中水声测距信息存在大量野值的问题,提出一种基于哺乳动物大脑海马体空间细胞认知机理启发的主从式AUV协同定位方法。首先,利用二维连续吸引子网络模型对网格细胞进行建模,并基于从艇速度与航向信息实现路径积分;其次,引入竞争性Hebb网络对多个网格细胞板自组织编码,从而基于多尺度网格细胞板加权生成具有单个放电活动包的位置细胞板;最后,将主从艇之间的水声测距量测信息转化为位置细胞放电活动,并利用其对位置细胞放电进行更新,从而校正从艇路径积分累积误差。由于位置细胞能够通过多次放电更新逐渐逼近真实位置,从而可以有效降低单一量测野值对于系统定位精度的影响。仿真实验结果表明,在水声测距量测噪声含有大量野值点而不满足高斯分布的情况下,所提出的类脑协同定位算法对量测噪声野值具有更高的鲁棒性,与有阈值判断的扩展卡尔曼滤波(EKF)协同定位算法相比定位精度提高28%。

Sonar Image Processing System for an Autonomous Underwater Vehicle(AUV)

Sonar image processing system is an important intelligent system of Autonomous Un-derwater Vehicle.Based on TMS320C30 high speed DSP,it is used to realize sonar imagecompression and underwater object detections including obstacle recognition in real time.Inthis paper,the software and hardware designs of this system are introduced and the experi-mental results are given.