Iterative Learning Control of Depollution Bioprocesses

The paper addresses the design and analysis of Iterative Learning Control (ILC) method for a wastewater biodegradation process. The bioprocess is considered to take place inside a continuous stirred tank bioreactor. The use of this kind of control methods is motivated by its advantages in the case of complex nonlinear systems like biotechnological processes. There were used two ILC algorithms - one considered a classical approach in the field (PD - type learning algorithm), and the second one which try to exploit some characteristics of the input signal (exponential learning algorithm). These control methods are implemented for the depollution control problem in the case of an anaerobic digestion process. This bioprocess is characterized by strongly nonlinear and not exactly known reaction rates. Furthermore, not all the state variables are measurable. The performance and effectiveness of the presented control algorithms are proven by simulation results.