Iterative learning control (ILC) is an effective control technique for motion systems that perform repetitively the same trajectory (setpoint). The result of the learning procedure is a feedforward signal that perfectly compensates all deterministic dynamics in the system for the learned setpoint performed at a specific start position. For other setpoints and start positions, the learned feedforward signal will not be perfect, because the learned deterministic dynamics are setpoint- and position-dependent. In this paper cogging compensating piecewise ILC (CCPILC) is proposed that enables to use one learned feedforward signal for different setpoints and start positions without losing performance. The learned feedforward signal will therefore be decomposed into a setpoint- and a position-dependent part, such that both parts can be adapted individually according to the desired change in setpoint and/or start position.