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Iterative learning control (ILC) is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference.