Almost all industrial robot applications are repetitive, performing the same task repeatedly. Learning control is the name attributed to a class of control techniques by which the system performance of a specified, repeatedly executing task is improved, based on the information acquired from previous executions. This is an advantage while controlling systems that are very difficult to model accurately. The ILC control schemes are structurally simple and computationally efficient. They possess two major advantages namely, the ability to reject unknown deterministic disturbances and the ability to handle uncertain systems.; An adaptive mechanism and a robust control technique (using mu-synthesis) were used for ILC implementation. The adaptive ILC technique offers practical solutions for the memory saving in real time applications while the robust approach gives a chance to make use of robust control techniques in ILC.; The thesis explains how adaptive and robust control approaches of iterative learning control were implemented. A study on System identification and model reduction techniques, along with the experimental results of Iterative Learning Algorithms applied to a 2-DoF planar Robot Manipulator will be discussed in this thesis.
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