Parameter learning for performance adaptation in large space structures

A parameter learning method is introduced which is used to broaden the region of operability of the adaptive control system of a space structure. The learning system guides the selection of control parameters in a process, leading to optimal system performance; the method is a form of learning by observation and discovery. It is applicable to any system where performance depends on a number of adjustable parameters. A mathematical model is not necessary as the learning system can be used whenever the performance can be measured via simulation or experiment. The results of a transient regulation experiment are presented. In this experiment, the learning system determines the best parameter values to use when given different disturbances.<
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