基于观测器的水下机器人神经网络自适应控制

高建树, 邢志伟, 张宏波

高建树, 邢志伟, 张宏波. 基于观测器的水下机器人神经网络自适应控制[J]. 机器人, 2004, 26(6): 515-518.
引用本文: 高建树, 邢志伟, 张宏波. 基于观测器的水下机器人神经网络自适应控制[J]. 机器人, 2004, 26(6): 515-518.
GAO Jian-shu, XING Zhi-wei, ZHANG Hong-bo. Observer-based Neural Network Adaptive Control of Underwater Vehicles[J]. ROBOT, 2004, 26(6): 515-518.
Citation: GAO Jian-shu, XING Zhi-wei, ZHANG Hong-bo. Observer-based Neural Network Adaptive Control of Underwater Vehicles[J]. ROBOT, 2004, 26(6): 515-518.

基于观测器的水下机器人神经网络自适应控制

详细信息
    作者简介:

    高建树(1966- ),男,工学硕士.研究领域:仪表与检测技术.

  • 中图分类号: TP24

Observer-based Neural Network Adaptive Control of Underwater Vehicles

  • 摘要: 给出了基于观测器的水下机器人神经网络自适应控制算法.控制算法由3部分组成:输出反馈控制、神经网络以及滑模项,其中输出反馈控制为了保证系统的初始稳定性;神经网络用于逼近系统的非线性动力学;滑模项用于补偿和抑制系统的外部扰动、神经网络逼近误差等.控制算法中所需要的速度量由状态观测器来提供.基于Lyapunov稳定理论给出了系统闭环稳定条件和稳定域.水池试验结果验证了算法的有效性.
    Abstract: Observer based neural network adaptive control(OBNC) algorithm for underwater vehicles is provided in this paper. The algorithm is composed of three parts: output feedback control, neural network and sliding mode item. Among them, the output feedback control is used to guarantee the stability of the system in initial phase, the neural network is used to approach the nonlinear dynamics of underwater vehicles, and the sliding mode item is used to compensate and bate the internal and external disturbances. The stable conditions and attraction region of the proposed observer based NN control algorithm is provided with Lyapunov based approach. The effectiveness of this control scheme is demonstrated with pool experiment.
  • [1] Yoerger D R, Slotine J J E. Robust trajectory control of underwater vehicles [J]. IEEE Journal of Oceanic Engineering, 1985,10 (4):462 470.
    [2] Fossen T I, Sagatun S I. Adaptive control of nonlinear systems: a case study of underwater robotic systems [J]. Journal of Robotic Systems, 1991,8(3): 393412.
    [3] Roberto C, Papoulias F A, Healey A J. Adaptive sliding mode control of autonomous underwater vehicles in the dive plane [J]. IEEE Journal of Oceanic Engineering, 1990, 15(3): 52160.
    [4] Corradini M L, Orlando G. A discrete adaptive variable structure controller for MIMO systems and its application to an underwater ROV [J]. IEEE Transactions on Control Systems Technology, 1997,5(3): 349359.
    [5] Antonelli G, Chiaverini S, Sarkar N, et al. Adaptive control of an autonomous underwater vehicle: experimental results on ODIN [J].IEEE Transactions on Control Systems Technology, 2001, 9 (5):756765.
    [6] Berghuis H, Nijmeijer H. A passive approach to controller-observer design for robot [J]. IEEE Transactions on Robotics and Automation, 1993, 9(6): 740754.
    [7] Sun F C, Sun Z Q, Woo P Y. Neural network-based adaptive controller design of robotic manipulators with an observer [J]. IEEE Transactions on Neural Networks, 2001,12( 1 ): 5467.
    [8] Fossen T I. Nonlinear passive control and observer design for ships [J] , Modeling, Identification and Control, 2000, MIC-21 ( 3 ): 129- 184.
    [9] Fossen T I , Strand J P. Passive nonlinear observer design for ships using Lyapunov methods: experimental results with a supply vessel (Regular Paper) [J]. Automatica, 1999,35 (1): 3 16.
    [10] Kim M H. Nonlinear control and robust observer design for marine vehicles [D]. USA: Virginia Polytechnic Institute and State University, 2000.
    [11] Funabashi K I. On the approximate realization of continuous mapping by neural networks [J]. Neural Network, 1989,2(3): 183192.
计量
  • 文章访问数:  31
  • HTML全文浏览量:  956
  • PDF下载量:  438
  • 被引次数: 0
出版历程
  • 收稿日期:  2004-04-06

目录

    /

    返回文章
    返回
    x 关闭 永久关闭