无奇异间接迭代学习控制及其在机器人运动模仿中的应用

针对相当广泛的一类非线性系统有限时间轨迹跟踪问题,提出了间接迭代学习方案.采用最小二乘算法,根据重复跟踪历史辨识非线性系统的线性化模型.利用一个分段学习方案可保证学习控制总在有效线性近似区域内进行.探讨了如何在学习过程中避免控制奇异问题,提出了一种高效的参数修正方法,保证输入耦合矩阵的估计行列式不为零.本文将这一控制方案应用于未知机器人及摄像机模型下的机器人运动模仿中,而不面临任何奇异问题.这是一个采用摄像机替代传统程序编写的新的机器人编程方法.

NONSINGULAR INDIRECT ITERATIVE LEARNING CONTROL AND ITS APPLICATION TO ROBOT MOVEMENT IMITATION

An indirect iterative learning control scheme for a general nonlinear system to track a trajectory with a finite time interval is presented, which uses the least square algorithm to identify the linearized model of the nonlinear system in terms of previous experiences of repetitive tracking. A segmented learning scheme is proposed to keep the learning only in the linear approximation region. How to avoid control singularity is discussed. A high efficient modification is proposed to ensure non zeso of the determinant of the estimated input matrix. The scheme is applied to robot movement imitation. Even without any knowledge about camera robot relationship, the controller faces no singularity. This might be a new way for robot trajectory programming by means of a camera instead of hard coding.