Learning identification control for model-based optoelectronic packaging

In this paper, we present a learning control algorithm for the packaging automation of optoelectronic systems. This automation provides high performance, low-cost alignment and packaging through the use of a model-based control theory and systemlevel modeling. The approach is to build an a priori model, specific to the assembled package's optical power propagation characteristics. From this model, an inverse model is created and used in the "feedforward" loop. In addition to this feedforward model, the controller is designed with feedback components, along with the inclusion of a built-in optical power sensor. We introduce a learning technique, which is activated at a lower sampling frequency for specific and appropriate tasks, to improve the model used in the model-based control. Initial results are presented from an experimental test bed that is used to verify the control and learning algorithms.