智能机器人基于作业子空间划分的自学习自适应控制方法

现有的机器人自适应控制基本上都是在建立机器人线性化的动力学模型的基础上,采用某种显式或隐式参数辨识的方法,在线地修正控制作用.本文针对机器人运动和动力学参数变化的固有特点,提出一种完全不同的自学习自适应方法.这种方法基于智能机器人分级系统中的两级结构,并且在空间域里而不是在时间域里处理机器人参数的变化.把机器人的作业空间划分成子空间,其中包括重力载荷的作用,每个子空间对应一组控制器.规划的轨迹映射到作业空间形成子空间序列.用自学习方法选择与这个序列对应的最佳控制器序列.该方法算法简单,计算量小.避开了通常的自适应方法遇到的一系列困难问题.

Most of the adaptive control approaches of robots are now based on the linearized dynamical models of therobots. They use some identification algorithms in order to estimate the parameters of the models explicitly orimplicitly and correct the control laws on line. In the paper, a learning-adaptive control method that is differentfrom the approaches above is presented in accordance with the inherent attributes of the movement of robotsthemselves and the variation of their dynamics parameters.a two-levels structure in the hierarchical control systemof intelligent robots is the basis of the method presented and the parameters variation of the robots is treated in thespacial domain not in the time one. The robot working space is divided several sub-spaces that contain the effectof gravity load and to every sub-space there correspond a set of controls. The robot path planed in the high levelof the two-levels structure mapping onto the working space, a sequence of sub-spaces is formed into and then acorrelative sequence of the optimal controls is picked out by means of a learning algorithm.

国家自然科学基金(B6875012)