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  

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

作　　者：庞哲楠 张国良 羊帆 贾枭 林志林 

机构地区：[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

出　　处：《计算机应用》2016年第10期2799-2805,2821,共8页journal of Computer Applications

基　　金：中国工程科技中长期发展战略研究项目(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。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.

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 

分 类 号：TP241[自动化与计算机技术—检测技术与自动化装置]