S^2 BHCA Multiple AUVs cooperation oriented control architecture

Oceanographic survey, or other similar applications should be the applications of multiple AUVs. In this paper, the skill ＆ simulation based hybrid control architecture （S^2BHCA） as the controller＇s design reference was proposed. It is a multi-robot cooperation oriented intelligent control architecture based on hybrid ideas. The S^2BHCA attempts to incorporate the virtues of the reactive controller and of the deliberative controller by introducing the concept of the ＂skill＂. The additional online task simulation ability for cooperation is supported, too. As an application, a multiple AUV control system was developed with three ＂skills＂ for the MCM mission including two different cooperative tasks. The simulation and the sea trials show that simple task expression, fast reaction and better cooperation support can be achieved by realizing the AUV controller based on the S^2BHCA.     

Design and evaluation of a hierarchical control architecture for an autonomous underwater vehicle

This paper researches on a kind of control architecture for autonomous undelwater vehicle （AUV）. After describing the hybrid property of the AUV control system, we present the hierarchical AUV control architecture. The architecture is organized in three layers： mission layer, task layer and execution layer. State supervisor and task coordinator are two key modules handling discrete events, so we describe these two modules in detail. Finally, we carried out a series of tests to verify this architecture The test results show that the AUV can perform autonomous missions effectively and safely. We can conclude the control architecture is valid and practical.     

水下潜器改进S面控制及控制系统仿真(英文)

S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV). However, it is difficult to adjust their control parameters manually. Choosing the optimum parameters for the controller of a particular AUV is a significant challenge. To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed. It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters. A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered. The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.     

Market based framework for multiple AUVs cooperation

A“Market” based framework for multiple AUVs team is introduced in this paper. It is a distributed meta-level task allocation framwork. The formulation and the basic concepts of the “Market” such as “goods” and “price” are discussed first, then the basic algorithm of the “auction”. The loosely coupled v-MDTSP tasks are considered as an example of the task allocation mission. A multiple AUV team controller and a detailed algorithm are developed for such applications. The simulation results show that the controller has the advantages such as robustness and low complexity and it can achieve better optimization results than the classical central controller （ such as GA） in some tasks. And the comparison of two different local solvers also implies that we should get the reasonable task allocation even not using the high quality algorithm, which can considerably decrease the cooperation computation.     

基于强化学习的自主式水下潜器障碍规避技术（英文）

Obstacle avoidance becomes a very challenging task for an autonomous underwater vehicle(AUV) in an unknown underwater environment during exploration process. Successful control in such case may be achieved using the model-based classical control techniques like PID and MPC but it required an accurate mathematical model of AUV and may fail due to parametric uncertainties, disturbance, or plant model mismatch. On the other hand, model-free reinforcement learning(RL) algorithm can be designed using actual behavior of AUV plant in an unknown environment and the learned control may not get affected by model uncertainties like a classical control approach. Unlike model-based control model-free RL based controller does not require to manually tune controller with the changing environment. A standard RL based one-step Q-learning based control can be utilized for obstacle avoidance but it has tendency to explore all possible actions at given state which may increase number of collision.Hence a modified Q-learning based control approach is proposed to deal with these problems in unknown environment.Furthermore, function approximation is utilized using neural network(NN) to overcome the continuous states and large statespace problems which arise in RL-based controller design. The proposed modified Q-learning algorithm is validated using MATLAB simulations by comparing it with standard Q-learning algorithm for single obstacle avoidance. Also, the same algorithm is utilized to deal with multiple obstacle avoidance problems.     

AUV控制系统试验平台

随着AUV(自主式潜器)技术的不断发展,对其自主性能的要求越来越高.AUV控制系统试验平台可以实现各种自主控制、动态控制算法及软件的系统集成.文章以能够自主完成地形勘察使命的AUV为研究对象,设计了AUV控制系统试验平台,对AUV控制系统硬件系统和基于行为仲裁的分层递阶软件系统做了较全面的介绍,利用QNX操作系统的多线程和消息传递技术实现了任务协调算法.在半实物试验平台上进行的地形勘察使命仿真结果验证了控制系统试验平台硬件结构合理,算法及软件集成可实现.     

水声对抗系统仿真体系架构分析

水声对抗系统仿真具有对抗平台、对抗目标和需仿真研究的关键技术多种多样的特点,因此在每种不同仿真任务下要能灵活地构建仿真系统.本文针对水声对抗系统仿真需求,对仿真系统的体系架构进行了研究,分析了基于CORBA标准的系统应用集成管理平台和基于HLA标准的协同仿真平台在水声对抗系统仿真中的应用.     

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.     

局部回归神经网络在水下机器人运动控制中的应用

探讨了水下机器人神经网络运动模型的结构,提出了带有局部回归结构的水下机器人神经网络控制器结构及预测控制的实现方法,给出了该神经网络的教师样本生成方法及学习方法,计算机仿真与水池实验结果验证了本文提出方法的有效性和可行性。     

水下智能潜器分布式控制系统设计与实现

分布式控制系统是水下管能潜器的重要部分．本文采用分层控制结构，即执行级、协调级和组织级，实现了潜器的控制系统。文中首先介绍了控制系统的组成，然后分别介绍了执行级采用神经网络实现自适应控制的方法，协调级中实现全局和局部路径规划的方法，以及组织级中基于行为分解的任务规划方法。文中给出了系统的仿真和实验结果。     

开发水下机器人控制器的MDA/MBSE方法的实用专业化研究（英文）

The model-driven architecture(MDA)/model-based systems engineering(MBSE) approach, in combination with the real-time Unified Modeling Language(UML)/Systems Modeling Language(SysML), unscented Kalman filter(UKF) algorithm, and hybrid automata, are specialized to conveniently analyze, design, and implement controllers of autonomous underwater vehicles(AUVs). The dynamics and control structure of AUVs are adapted and integrated with the specialized features of the MDA/MBSE approach as follows. The computation-independent model is defined by the specification of a use case model together with the UKF algorithm and hybrid automata and is used in intensive requirement analysis. The platform-independent model(PIM) is then built by specializing the real-time UML/SysML's features, such as the main control capsules and their dynamic evolutions, which reflect the structures and behaviors of controllers. The detailed PIM is subsequently converted into the platform-specific model by using open-source platforms to quickly implement and deploy AUV controllers. The study ends with a trial trip and deployment results for a planar trajectory-tracking controller of a miniature AUV with a torpedo shape.     

智能水下机器人的建模和控制策略研究(英文)

To provide a suitable model for AUV simulation and control purposes, a general nonlinear dynamic model including a novel thruster hydrodynamics model was derived. Based on the modeling method, the “AUV-XX” simulation platform was established to carry out fundamental tests on its motion characteristics, stability, and controllability. A motion control strategy consisting of both position and speed control in a horizontal plane was designed for different task assignments of underwater vehicles. Combined control of heave and pitch was adopted to compensate for the reduction of vertical tunnel thrusters when the vehicle is moving at a high speed. An improved S-surface controller based on the capacitor plate model was developed with flexible gain selections made possible by different forms of restricting the error and changing the rate of the error. Simulation results show that the derived general mathematical model together with simulation platform can provide a test bed for fundamental tests of motion control. Additionally, the capacitor plate model S-surface control shows a good performance in guiding the vehicle to achieve the desired position and speed with sufficient accuracy.     

H∞ Robust Fault-Tolerant Controller Design for an Autonomous Underwater Vehicle＇s Navigation Control System

In order to improve the security and reliability for autonomous underwater vehicle (AUV) navigation, an H_∞ robust fault-tolerant controller was designed after analyzing variations in state-feedback gain. Operating conditions and the design method were then analyzed so that the control problem could be expressed as a mathematical optimization problem. This permitted the use of linear matrix inequalities (LMI) to solve for the H_∞ controller for the system. When considering different actuator failures, these conditions were then also mathematically expressed, allowing the H_∞ robust controller to solve for these events and thus be fault-tolerant. Finally, simulation results showed that the H_∞ robust fault-tolerant controller could provide precise AUV navigation control with strong robustness.     

微小型潜器空间运动建模与仿真(英文)

对自主式潜器空间运动进行精确建模和仿真对研究其操纵和控制特性有重要意义,本文以开发的"MAUV-Ⅱ"微小型潜器为对象,基于动量定理和动量矩定理建立了潜器空间运动的非线性数学模型,将潜器受力分解为各个模块并表达为矩阵形式.在运动非线性数学模型的基础上,结合虚拟现实技术建立了运动仿真系统,针对所研究潜器的特点,采用S面控制方法对此"MAUV-Ⅱ"水下运动的艏向控制和深度控制进行了仿真研究,同时进行了基于目标规划的长距离航行仿真试验.仿真结果反映了潜器具有较好的空间操纵性能,也验证了控制软件的可行性和可靠性.     

高阶CMAC神经网络及其在水下机器人运动控制中的应用

对高阶ＣＭＡＣ神经网络的结构和工作原理进行了研究,提出了中间层作用函数地址的计算方法,给出了计算高阶基函数的不同方法,利用高阶ＭＣＡＣ神经网络对水下机器人模糊深度控制器进行了学习,仿真结果显示了不同高阶基函数ＭＣＡＣ网络的不同建模能力,证明了中间层作用函数地址的计算方法正确。     

带吊舱水下机器人运动建模与仿真(英文)

To provide a simulation system platform for designing and debugging a small autonomous underwater vehicle’s (AUV) motion controller, a six-degree of freedom (6-DOF) dynamic model for AUV controlled by thruster and fins with appendages is examined. Based on the dynamic model, a simulation system for the AUV’s motion is established. The different kinds of typical motions are simulated to analyze the motion performance and the maneuverability of the AUV. In order to evaluate the influences of appendages on the motion performance of the AUV, simulations of the AUV with and without appendages are performed and compared. The results demonstrate the AUV has good maneuverability with and without appendages.     

基于动态神经网络的AUV航向自适应控制

海洋环境的复杂性以及自身模型的不确定性,给自主潜航器(AUV)航向控制带来很大困难.针对AUV的特点和控制方面所存在的问题,采用了带衡量因子的动态BP神经网络控制器控制AUV的航向.理论分析和仿真结果表明,与传统的PID控制器相比,在扰动存在的情况下,神经网络控制器具有更好的自适应性和鲁棒性.     

人工神经网络控制器在水下无人运载器导航中的应用

本文以水下无人运载器（AUV）绕平面圆周航行的速度、位置控制为例，推广到任何轨迹的控制问题。利用神经网络学习AUV运行的内在规律，预测未来一步的运行情况，并用改良的PID方式前馈与后馈相结合控制其执行机构。系统学习、预测及PID各增益量利用GESA（GuidedEvolutionarySimulatedAnnealing）全局优化方法求得。本方法具有自适应性、强非线性及前馈控制等特点，优于其它一般的控制器。     

一类复合模糊控制器在舰炮随动系统中的应用

舰炮随动系统的快速性,稳定性和控制精度是影响舰炮武器系统作战性能的重要因素。针对舰炮随动系统参数时变性和负载干扰大对其快速性,稳定性和准确性的影响,设计了一种P＋FUZZY-PI复合控制器。建立了该控制器的仿真模型,并以某型舰炮为例,分别采用传统PID控制器和P＋FUZZY-PI复合控制器进行了计算机仿真。通过比较仿真结果,表明该复合控制器调节时间短、控制精度高、鲁棒性强。该类控制器能够在舰炮随动控制中获得良好的控制性能。     

AUV均衡系统设计及垂直面运动控制研究

针对AUV传统均衡调节系统存在难以克服的缺陷而无法实用的问题,设计了一种新型液压均衡系统。该系统主要由油箱和安装在AUV轴线上的两个油囊组成,通过调节油箱和油囊的油量来调节AUV的姿态和浮态。同时在简化AUV垂直面运动模型的基础上,构建了深度控制、纵倾控制和浮态控制模型,并基于PID和均衡系统设计了深度控制、纵倾控制和浮态控制方法。仿真结果表明在无干扰和有干扰两种情况下,设计的均衡系统能有效控制AUV的垂直面运动;建立的AUV垂直面运动控制模型符合AUV运行特性;PID控制算法适合海流干扰下的AUV均衡控制,且具有良好的控制品质。