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提出了一种针对自学习控制的稳定性判据，应用这一稳定性判据将自学习控制器的设计转化为寻找正定离散矩阵核，从而回答了两个问题，其一什么样的量可以通过自学习叠代加以控制，其二学习叠代中什么样的滤波环节的引入不会影响学习收敛性．根据这一判据设计了一种机器人参数自学习控制律，它保证跟踪轨线全程的收敛性．
A stability criterion for self-learning control is proposed. By applying this stability criterion, the design of self-learning controller is transformed into finding the positive definite discrete matrix kernel, thus answering two questions: what kind of quantity can be By self-learning iteration to be controlled, the second learning iteration in the introduction of what kind of filtering does not affect the learning convergence. According to this criterion, a robot parameter self-learning control law is designed, which guarantees the convergence of tracking trajectory.