Fuzzy iterative learning control applied in a biological reactor using a reduced number of measures

There exist some processes difficult to control as the chemical ones, a common problem takes place when the output cannot be measured on-line, and so, closed-loop control cannot be implemented. In this work an iterative learning control type proportional-derivative is analyzed and theoretical results are shown, this control is applied to a biological reactor to degrade phenol by working in discontinuous batch state, as the measures of the substrata concentrations are taken by hand, it was proposed to have a sample time of one hour. To guarantee convergence and to improve the control, cubic splines were used to interpolate the measures. Fuzzy logic was used to compute the control gains used to build the control signal. Simulation results are shown and the control signals are presented through iterations, here it is possible to see that the error is smaller using fuzzy logic to compute the control signal when iterations run. 2014 Elsevier Inc. All rights reserved.