Iterative learning control with input parameterization of a fedbatch lactic acid fermentation process

This paper presents a special type of iterative learning control (ILC) strategy for the optimization of a fedbatch lactic acid fermentation process. The main advantage of using ILC is that it uses only off-line output measurements, circumventing the well-known drawback of insufficient on-line measurement capability in many in situ fermentation control applications. Due to the fact that fedbatch reactors are permanently in transient regime, the tracking performances of a conventional ILC algorithm deteriorated as the number of off-line samples decreases. Improved results are obtained by using an ILC approach based on the parameterization of the control profile with an adequate piece-wise continuous exponential function. A simulation study demonstrates the feasibility of the proposed ILC strategy.

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