<h2>AAS 97-150</h2>
<h2>CONDITIONS FOR CONVERGENCE IN INDIRECT ADAPTIVE CONTROL  BASED LEARNING CONTROL                                                                  </h2>
<h4>  H. P. Wen and R. W. Longman, Columbia University                                                                                                                        </h4>
<h2> Abstract </h2>
 Learning control develops controllers that learn from previous experience  performing commands in order to improve their performance in future executions.   One form of learning control is based on digital indirect adaptive control  concepts.  The adaptive problem normally requires knowledge that the system is  stable when inverted, i.e., all zeros are inside the unit circle.  This is very  constraining since discretization often introduces zeros outside the circle.  In  this paper we show that the requirement doesn't apply to the learning control  problem, and the finite time nature of the repeating process allows substituting an  alternate proof that eliminates the assumption.