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首页> 外文学位 >Neural network model based control of nonlinear flexible link systems (Predictive control, Robotics).
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Neural network model based control of nonlinear flexible link systems (Predictive control, Robotics).

机译：基于神经网络模型的非线性柔性链接系统控制（预测控制，机器人技术）。

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摘要

The presence of link flexibilities in multilink manipulators increases the system order by the number of modes retained when the assumed modes approach is taken or by the number of independent coordinates of a finite element description of the elastic member. The resulting model is a highly coupled nonlinear model of higher complexity than the rigid manipulator model. Such complex dynamics are ignored in non-model based controllers which are simpler and limited in performance compared to model based controllers. Recently, model based predictive controllers based on linear models have been shown to give performance improvements over non-model based controllers in plants with variable dead times and non-minimum phase effects. When the plant is highly nonlinear, it can be advantageous to consider a nonlinear model such as the multilayer perceptron (MLP) network that can approximate to arbitrary precision the nonlinearities over an operating range. Based on recursive long range predictions of the neural model, an input sequence that minimizes a cost function is approximated by a second order descent method. This is equivalent to a Backpropagation where the derivatives are calculated with respect to future control inputs rather than the network weights. The predictive control framework allows variations in model order, variable dead times and nonminimum phase effects in the plant.; The modeling part presents a derivation of the equations of motion based on finite element analysis that results in a more structured set of equations for the flexible link manipulator compared to previous derivations. The new set of equations preserves the structure of the rigid manipulator model but single terms corresponding to a rigid linkage are expanded into submatrices of a chosen dimension when link flexibilities are introduced. Furthermore, the new dynamic equations satisfy the passivity properties needed for many nonlinear adaptive control schemes.
机译：多链接操纵器中链接灵活性的存在通过采用假定的模态方法时保留的模态数量或弹性构件的有限元描述的独立坐标数量增加了系统顺序。结果模型是高度耦合的非线性模型，其复杂度高于刚性操纵器模型。在基于非模型的控制器中，这种复杂的动力学被忽略了，与基于模型的控制器相比，非模型的控制器更简单且性能受到限制。最近，在具有可变死区时间和非最小相位效应的工厂中，基于线性模型的基于模型的预测控制器已显示出比基于非模型的控制器更好的性能。当工厂是高度非线性的时，考虑一个非线性模型（例如多层感知器（MLP）网络）可以使操作范围内的非线性近似到任意精度可能是有利的。基于神经模型的递归远程预测，可通过二阶下降法近似使成本函数最小的输入序列。这等效于反向传播，在反向传播中，是根据将来的控制输入而不是网络权重来计算导数。预测控制框架允许工厂中模型顺序，可变死区时间和非最小相位效应发生变化。建模部分提供了基于有限元分析的运动方程式的推导，与先前的推导相比，该方法为柔性连杆操纵器带来了更为结构化的方程组。新的方程组保留了刚性机械手模型的结构，但是当引入链接灵活性时，对应于刚性连杆的单个项被扩展为选定尺寸的子矩阵。此外，新的动力学方程式满足了许多非线性自适应控制方案所需的无源特性。

著录项

  • 作者

    Song, Bumjin.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 130 p.
  • 总页数 130
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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