计算机应用 ›› 2016, Vol. 36 ›› Issue (10): 2799-2805.DOI: 10.11772/j.issn.1001-9081.2016.10.2799

• 人工智能 • 上一篇    下一篇

力矩受限的柔性空间机器人模糊神经网络自适应跟踪控制及振动抑制

庞哲楠1, 张国良1, 羊帆1,2, 贾枭1, 林志林1   

  1. 1. 火箭军工程大学 控制工程系, 西安 710025;
    2. 宝鸡市高新技术研究所, 陕西 宝鸡 721000
  • 收稿日期:2016-04-11 修回日期:2016-06-14 出版日期:2016-10-10 发布日期:2016-10-10
  • 通讯作者: 庞哲楠,E-mail:pznfatfight@163.com
  • 作者简介:庞哲楠(1992—),男,浙江杭州人,硕士研究生,主要研究方向:空间机器人;张国良(1970—),男,四川金堂人,教授,博士,主要研究方向:先进控制理论、机器人、多智能体协同控制;羊帆(1985—),男,陕西汉中人,博士研究生,主要研究方向:先进控制理论、空间机器人;贾枭(1993—),男,陕西渭南人,硕士研究生,主要研究方向:空间机器人、多智能体协同控制;林志林(1993—),男,福建漳州人,硕士研究生,主要研究方向:空间机器人。
  • 基金资助:
    中国工程科技中长期发展战略研究项目(2014-zcq-10)。

Adaptive tracking control and vibration suppression by fuzzy neural network for free-floating flexible space robot with limited torque

PANG Zhenan1, ZHANG Guoliang1, YANG Fan1,2, JIA Xiao1, LIN Zhilin1   

  1. 1. Department of Control Engineering, Rocket Force University of Engineering, Xi'an Shaanxi 710025, China;
    2. Baoji New High Tech Research Institute, Baoji Shaanxi 721000, China
  • Received:2016-04-11 Revised:2016-06-14 Online:2016-10-10 Published:2016-10-10
  • Supported by:
    BackgroundThis work is partially supported by the Long-term Development Strategy Research Project of Chinese Engineering Science and Technology (2014-zcq-10).

摘要: 针对力矩受限和存在参数不确定情况下,自由漂浮柔性空间机器人(FFFSR)关节轨迹跟踪控制与柔性振动抑制的问题,利用奇异摄动法将系统分解为关节轨迹跟踪的慢变子系统和描述柔性振动的快变子系统,进而提出含慢、快变控制项的组合控制器。对于慢变子系统,设计一种无需模型的模糊径向基函数(RBF)神经网络(FRBFNN)自适应跟踪控制方案,利用神经网络观测器估计关节角速度信息,并对系统的未知非线性函数进行逼近;对于快变子系统,采用扩张状态观测器(ESO)对不易测量的柔性模态坐标导数和不确定扰动进行估计,并结合线性二次调节器(LQR)方法抑制柔性振动。数值仿真结果表明,当控制力矩限制在±20 N·m和±10 N·m范围内时,该组合控制器能够在2.5 s实现稳定的关节轨迹跟踪,并将柔性振动幅值限制在±1×10-3 m内。

关键词: 自由漂浮柔性空间机器人, 奇异摄动法, 模糊神经网络控制, 扩张状态观测器, 力矩受限, 不确定性

Abstract: Joint trajectory tracking control and flexible vibration suppression techniques for a Free-Floating Flexible Space Robot (FFFSR) were discussed under parameter uncertainty and limited torque. A composite controller containing a slow control subsystem for joint trajectory tracking and a fast control subsystem for flexible vibration description were proposed using singular perturbation method. A model-free Fuzzy Radial Basis Function Neural Network (FRBFNN) adaptive tracking control strategy was applied in the slow subsystem. FRBFNN was adopted to support the estimation of velocity signals performed by the observer, the approximation of the unknown nonlinear functions of the observer as well as the controller. The fast subsystem adopted an Extended State Observer (ESO) to estimate coordinate derivatives of flexible modal and uncertain disturbance, which could hardly be measured, and used Linear Quadratic Regulator (LQR) method to suppress the flexible vibration. Numerical simulation results show that the composite controller can achieve stable joint trajectory tracking in 2.5 s, and the flexible vibration amplitude is restricted in ±1×10-3 m, when the control torque is limited within ±20 N·m and ±10 N·m.

Key words: Free-Floating Flexible Space Robot (FFFSR), singular perturbation method, fuzzy neural network control, Extended State Observer (ESO), limited torque, uncertainty

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