A novel type of iterative learning control (ILC) strategy for the optimization of fedbatch fermentation processes is developed and applied to lactic acid production as case study. Due to the fact that fedbatch reactors are permanently in transient regime, the tracking behavior of conventional ILC deteriorates as the number of off-line measurements decreases. The first contribution of this study was to use an interpolation method in order to reconstruct the input continuity between the measurement samples. Then, control performance was improved by using an ILC algorithm based on the parameterization of the input profile with piecewise continuous exponential functions that are specially adequate for fedbatch processes. Furthermore, the proposed learning rule was able to track accurately the process output reference with only two off-line measurements. A simulation study demonstrates the feasibility of the ILC approach.