A nonlinear predictive control of processes with multiscale objectives using a fuzzy-system identification approach

In this paper the problem of model based control of a microscopic process is investigated. The unavailability of closed-form models as well as the ill-definition of variables to describe the process evolution makes the controller design task challenging. We address this problem via a fuzzy system identification of the dominant process dynamics. The data required for the system identification of such processes is produced employing atomistic simulations. A methodology is developed in which fuzzy logic for nonlinear system identification is coupled with nonlinear model predictive Control for control of microscopic processes. We illustrate the applicability of the proposed methodology on a Kinetic Monte Carlo (KMC) realization of a simplified surface reaction scheme that describes the dynamics of CO oxidation by O₂ on a Pt catalytic surface. The nonlinear fuzzy model gives a good approximation to the system even without using filter for the system and the proposed controller successfully forces the process from one stationary state to another state.