基于CAN总线的自主水下航行器分布式控制系统设计

提出一种基于CAN总线的分布式控制系统设计方案,该总线主要搭载任务控制系统、导航控制系统、运动控制系统、安保系统四个节点。将自主水下航行器的功能分布到不同的控制节点,具有较好的体系结构,且便于维护与升级。不同于集中式控制系统,当系统内需增加其他功能模块时,无需修改硬件部分可直接接入控制系统,便于控制具有很好的实时性与可扩展性。最后通过湖试结果显示,自主水下航行器航行稳定,分布式控制系统运行良好,验证了该设计的可靠性与有效性。     

混合驱动水下航行器水平推进模式水动力特性

关于水下航行器结构设计优化问题,混合驱动水下航行器(HUG)是一种新型水下航行器,实现了自治式水下航行器(AUV)与水下滑翔机(AUG)结构和功能的集成,具有航速大、航程长、机动性好等特点.混合驱动水下航行器所受水阻力与机动性是其水平推进模式(AUV模式)下的重要性能指标.针对混合驱动水下航行器的要求与结构特点,采用计算流体力学方法,分析了航行器在AUV模式下的航行水阻力和机动性.研究表明,混合驱动水下航行器在集成AUV与AUG功能优势的同时,在AUV运行模式下,航行阻力将增大约30％,航行机动性降低约15 ～25％.结果为混合驱动水下航行器的外形设计提供了依据.     

基于网络控制系统的水下航行器制导系统结构

研究基于网络控制原理的水下航行器制导系统结构,详细分析了基于时间触发的TFCAN高层协议在水下航行器网络控制系统中的应用;针对目前水下航行器制导系统的控制现状,提出基于NCS的水下航行器制导系统一般性结构和设计方法,有效地解决了水下航行器制导系统中信息传送的实时性和信息共享性问题,同时论证了水下航行器网络控制系统中影响其系统稳定性的时延问题：论文旨在通过对水下航行器网络控制系统结构的研究,指出其结构的意义和进一步的研究领域。     

CAN总线在水下航行器导航与控制系统中的应用

导航和控制系统是水下航行器的重要组成部分,CAN总线是现场总线和分布式实时控制的发展趋势.针对传统采用RS422通信的集中式导航控制系统中控制管理中心任务过重、可靠性差、抗干扰 能力差等缺点,设计了基于CAN总线的分布式导航和控制系统.首先给出了分布式系统的构建方案;再针对基于PC104的控制管理中心、预置器与CAN总...     

水下航行器网络控制系统仿真

传统控制系统的设计方法中忽略了通信网络中的时延和数据包丢失等问题,仅通过传统方法设计的控制器来降低其对控制系统产生的不利影响,严重影响了系统的稳定性;对于水下航行器等对系统性能要求较高的水下控制平台,突破传统使其在网络环境下能够稳定运行显得尤为重要;在此背景下,提出了网络控制系统的设计方案,以水下航行器为控制平台,进行系统建模,设计反馈控制器,使用MATLAB仿真工具TrueTime,研究分析了网络体系结构下时延和丢包对传统控制系统动静态性能的影响;仿真结果表明该设计方法优化了系统性能,为系统在发生网络诱导时延和数据包丢失时能够稳定运行,提供了可靠的参考依据;该设计结果具有普适性,也可以用于导弹、坦克等航行器。     

水下航行器控制系统半实物仿真

该文提出了一种水下航行器控制系统半实物仿真结构,介绍了仿真数学模型与仿真算法,并给出了水下航行器控制系统半实物仿真实例。实航试验结果表明半实物仿真与水下航行器控制系统实际航行结果基本接近。     

水下超高速航行器首舵控制系统的设计

水下超高速航行器处于巡航阶段时,由于其大部分表面被超空泡包裹,运动模式不同于常规的全沾湿航行器;为了对超高速水下航行器的定深弹道实施控制,在研究水下超高速航行器运动模型的基础上,对首舵机控制系统的控制率进行了综合,研制了一种新型的深度间接控制的电动首舵机闭环控制系统;详细介绍了功率控制单元的工作原理、硬件设计的关键技术及控制软件设计;地面考核试验证明该控制系统完全满足超高速航行器的姿态控制要求;研究结果可为水下超高速航行器控制系统的设计提供参考。     

基于CANopen协议的水下航行器控制系统现场总线应用设计

分析了CAN现场总线技术及特点，针对某高速水下航行器控制系统数据交换实时性需求，结合当前采用的“自定义CAN高层协议”存在的问题，从解决系统稳定性，降低人为干扰因素，提高可靠性及后续网络扩展等方面考虑，提出一种基于CANopen标准高层协议的水下航行器控制系统网络架构，研究了控制系统网络结构及特点，描述了主要网络节点的功能及任务，建立了CANopen通信模型，制订了CANopen高层通讯协议，在项目研制中应用实践，开展了系统性对比测试，实施了全航行器系统、满负荷水下实航试验，结果表明CANopen控制网络运行快捷、稳定可靠，数据交换流畅，网络负载率仅为21.9%，明显优于国际现场总线网络协会要求，适合推广应用至UUV等多种水下航行器系统。     

基于MSP430的遥控无人水下航行器控制系统设计

针对某型遥控无人水下航行器,给出了总体结构和系统组成;在此基础上,基于MSP430微控制器,给出了某型低功耗的无人水下航行器控制系统的总体设计方案,采用H桥电路实现对航行器电机的控制,用nRF905单片无线收发器进行通信实现遥控控制,并进行了软件的详细设计与调试。水池试验结果表明,某型无人遥控水下航行器以约0.5m/s的速度前进时,运行情况良好,并基本实现了拐弯、下潜和上升等各预期功能,最后得出了该控制方案是可行的结论。     

基于网络控制系统的水下自主航行器制导系统结构

在现代控制系统中,网络控制系统可以用最小成本实现柔性系统,该柔性系统可以完成包括功能可重新配置的许多任务;网络控制系统采用公用的总线结构,使得系统具有更加有效的灵活功能、更好的资源共享、降低系统的维护成本,网络控制系统的最大问题是由于网络而产生的时延和数据包的丢失;研究了基于网络控制原理的水下自主航行器制导系统,详细分析了水下自主航行器网络控制系统的稳定性问题和基于时间触发的TTCAN高层协议;论文旨在通过对水下自主航行器网络控制系统结构的研究,指出其结构的意义和进一步的研究领域.     

基于模糊混合控制的自治水下机器人路径跟踪控制

基于模糊混合控制策略,本文提出了一种用于非线性欠驱动自治水下机器人的鲁棒路径跟踪控制方法.利用Sugeno型模糊推理系统,将PD滑模控制器与非奇异终端滑模控制器光滑连接,构造了模糊混合控制器.它能充分融合这两类控制器的优势,无论系统远离平衡点还是在其附近,都能取得快速收敛的效果.如果,借助于非时间参考量,将该混合控制器用于自治水下机器人路径跟踪控制,将有利于提高它在不确定环境中的跟踪能力.最后,通过仿真计算结果验证了该控制策略的有效性.     

Adaptive tracking control of an autonomous underwater vehicle

This paper presents the trajectory tracking control of an autonomous underwater vehicle(AUV). To cope with parametric uncertainties owing to the hydrodynamic effect, an adaptive control law is developed for the AUV to track the desired trajectory. This desired state-dependent regressor matrix-based controller provides consistent results under hydrodynamic parametric uncertainties.Stability of the developed controller is verified using the Lyapunov s direct method. Numerical simulations are carried out to study the efficacy of the proposed adaptive controller.     

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.     

Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control

This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.     

Design method for a new control system for an autonomous underwater vehicle using linear matrix inequalities

The independent administrative corporation Japan Agency for Marine–Earth Science and Technology (JAMSTEC) has developed a small light autonomous underwater vehicle (AUV) named marine robot experimental 1 (MR-X1).¹ The motion control of MR-X1 is considered in this article. Since the dynamics of MR-X1 mainly depends on its own speed, the motion control is a nonlinear control system. We propose a new controller design method for this system using linear matrix inequalities (LMIs). This algorithm gives a solution as a linear matrix inequality, and can be adapted to solve many LMIs simultaneously. LMIs can be obtained by substituting several speeds into the dynamics of the MR-X1. The proposed controller, which can be derived from the solution of the LMIs, was adapted to MR-X1 and showed good performance in experiments. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

Maneuvering and control of a biomimetic autonomous underwater vehicle

This work presents an approach for maneuvering and controlling a biomimetic autonomous underwater vehicle (BAUV). The BAUV swims forward by oscillating its body and caudal fin. It turns by bending its body and caudal fin toward the intended direction of motion. A body-spline function is specified by a set of parameters. Genetic algorithms are then used to find the values of the parameter by evaluating a fitness function over several swimming trials in a water tank. The fitness function is defined as the ratio of the kinetic energy of the forward motion to the required driving power of the joint motors. A control law that uses the oscillating frequency to control the forward speed, and applies a body-spline offset parameter to control the yawing rate is proposed. Moving averages of swimming speeds and heading angles are utilized as feedback signals to control the forward speed and heading angle of the BAUV. The effectiveness of the control algorithm is experimentally confirmed.

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.     

一种基于混沌过程神经元水下机器人运动控制方法

由于系统的强非线性以及不确定性,同时考虑到港湾环境下水声信号的噪声大,水下机器人进行精确作业时的运动控制一直是其实用化过程中困挠人们的问题。过程神经网络是传统神经网络的拓展,它增加了一个对于时间的聚合算子,使网络同时具有时空二维信息处理能力,从而更好地模拟了生物神经元的信息处理机制。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。在基本神经元模型上,结合S函数和预先规划思想,建立水下机器人过程神经元运动控制模型,参数学习过程中,将遍历性的渐变混沌噪声引入其中,增强控制器全局优化能力。仿真试验表明,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、鲁棒性好等各种优点。     

Underwater docking of an under-actuated autonomous underwater vehicle: system design and control implementation

Underwater docking greatly facilitates and extends operation of an autonomous underwater vehicle (AUV) without the support of a surface vessel. Robust and accurate control is critically important for docking an AUV into a small underwater funnel-type dock station. In this paper, a docking system with an under-actuated AUV is presented, with special attention paid to control algorithm design and implementation. For an under-actuated AUV, the cross-track error can be controlled only via vehicle heading modulation, so both the cross-track error and heading error have to be constrained to achieve successful docking operations, while the control problem can be even more complicated in practical scenarios with the presence of unknown ocean currents. To cope with the above issues, a control scheme of a three-hierarchy structure of control loops is developed, which has been embedded with online current estimator/compensator and effective control parameter tuning. The current estimator can evaluate both horizontal and vertical current velocity components, based only on the measurement of AUV’s velocity relative to the ground; in contrast, most existing methods use the measurements of both AUV’s velocities respectively relative to the ground and the water column. In addition to numerical simulation, the proposed docking scheme is fully implemented in a prototype AUV using MOOS-IvP architecture. Simulation results show that the current estimator/compensator works well even in the presence of lateral current disturbance. Finally, a series of sea trials are conducted to validate the current estimator/compensator and the whole docking system. The sea trial results show that our control methods can drive the AUV into the dock station effectively and robustly.     

多自主水下航行器系统一致性编队跟踪控制

研究了自主水下航行器的编队路径跟踪问题.基于无源性理论与一致性跟踪理论,在仅有部分AUV获取编队速度信息情形下,设计一种分布式控制律,实现了集群AUV的一致性编队跟踪.控制律分为2个部分:一部分基于无源性同步原理,建立了协同误差到跟踪误差的无源性通道;另一部分为一致性协同跟踪控制器,保证每个AUV相对于虚拟领航者的不一致参考信息通过协商达到最终一致状态.文章应用Nested Matrosov定理证明了整个闭环系统的稳定性,仿真结果验证了上述方法的有效性和可行性.