Filtering in adaptive control of distributed parameter bioreactors in the presence of noisy measurements

Abstract

This paper deals with simulation experiments on the model-based adaptive control of the distributed parameter bioreactor in the presence of the measurement noise. Since in such cases the adaptive controller itself is not able to ensure good control performance, there is a need to propose more sophisticated application. It is based on the first-order digital low-pass filters and allows us to decrease the influence of the measurement noise on the control performance. In order to decrease this influence it is possible to adjust three parameters of the application: controller tuning parameter, forgetting factor for the estimation of the substrate consumption rate and time constant of the low-pass filters. Simulation results, presented in this paper, have been obtained for different values of these parameters and, based on these results, it is possible to propose a robust application for the adaptive control of the bioreactor with optimally chosen values of the adjustable parameters. This application can be suggested to be applied in the adaptive control of a real industrial distributed parameter bioreactor with noisy measurement data.