项目名称: 基于非因果稳定逆的柔性机械臂学习控制
项目编号: No.61273133
项目类型: 面上项目
立项/批准年度: 2013
项目学科: 自动化技术、计算机技术
项目作者: 刘山
作者单位: 浙江大学
项目金额: 80万元
中文摘要: 本项目主要研究柔性机械臂末端轨迹精确跟踪的学习控制问题。针对柔性机械臂柔性结构和欠驱动造成的非最小相位特点,主要采用非因果稳定逆技术保证柔性臂末端的跟踪精度。期望以Euler-Bernouli梁模型为基础,结合Lagrange能量法与假设模态法,给出柔性臂动力学方程。针对模型的不确定性,放松对运行轨迹跟踪的要求,采用将非因果逆与模型预测相结合的学习方法,在线修正模型参数,实现学习、规划和控制一体的柔性臂路径精确跟踪控制,保证柔性臂末端路径与目标路径完全一致。在柔性臂重复运行的条件下,与迭代学习控制技术相结合,采用非因果的迭代学习律结构,根据最优化性能指标和稳定逆理论,采用以前系统运行的数据构造在实际应用中能够实现的系统稳定逆,实现柔性臂的轨迹精确跟踪控制。所提出的控制技术将在实际柔性臂系统上进行实验验证。旨在为柔性机械臂在实际生产中推广应用提供技术保障。
中文关键词: 柔性机械臂;非因果稳定逆;模型预测方法;迭代学习控制;基函数逼近
英文摘要: In this project, the learning control for trajectory tracking problems of flexible manipulator's endpoint is mainly studied. The non-minimum phase behaviour of flexible manipulator is exhibited by the flexible structures and underactuated character. Noncausal stable inversion method to cope with this will be mainly adopted to guarantee the running accuracy of control systems. Flexible manipulator dynamics equations based on Euler-Bernouli beam model will be developed through the assumed mode method and Lagrange equation fomulation. Under model uncertainties, the requirements of accurate tracking performance will be relaxed for the control system to ensure the system path to match the target path exactly. A learning method on parameter modification of the system model will be proposed based on noncausal stable inversion combined with model predictive method, which provided a technique to achieve precise path tracking control of flexible manipulator. Under the condition of the system is running repetitively, with a combination of iterative learning control (ILC) technology, using noncasusal iterative learning structure, an accurate trajectory tracking control algorithm will be given according to optimum performance index, which constructs the noncausal ILC law to improve the rate of convergence and the accuracy of
英文关键词: flexible manipulator;noncausal stable inversion;model predictive method;iterative learning control;basis function approximation