摘要
分析了初始控制输入量对迭代学习控制稳定性和收敛速度的影响,提出充分利用系统以往的控制经验来确定迭代学习初始控制输入量的思想,并给出3类确定方法——线性加权法、拟合曲线法和智能化法.对机器人对象的仿真结果表明,恰当地选取初始控制输入量,可使系统以较小的误差对新任务进行跟踪,进而减少迭代次数,提高学习控制的收敛速度,增强对新环境、新任务的适应能力.
The effects of the initial value of iterative learning control inputs to the convergence and stability are discussed. An idea of acquiring these values based on the system's control experience is presented. At the same time, three kinds of methods of getting these values - linear weighted average, curve fitting and intelligent methods, are given. Simulation results of a robotic system show that the robotic system can track the new task with less error at the first trial if the initial control input is chosen properly. So the trials of learning is decreasing, and the convergence and capacity of adapting to the new task and new environment are improved.
出处
《控制与决策》
EI
CSCD
北大核心
2004年第1期27-30,35,共5页
Control and Decision
基金
国家自然科学基金资助项目(69975003)
湖南省自然科学基金资助项目(98JJY2044).
