Integrating data-based modeling and nonlinear control tools for batch process control

Abstract

This work presents a data-based multi-model approach for modeling batch systems in which multiple local linear models are identified using partial least squares (PLS) regression and then combined with an appropriate weighting function that arises from fuzzy c-means clustering. The resulting data-based model is used to generate estimates of empirical reverse-time reachability regions (RTRRs) (defined as the set of states from where the data-based model can be driven inside a desired end-point neighborhood of the batch system) using an optimization based algorithm. The empirical RTRRs are used to formulate a computationally efficient predictive controller with inherent fault-tolerant characteristics. Simulation results of a fed-batch reactor subject to noise, disturbances, and uncertain parameters demonstrate that the empirical RTRR-based MPC design consistently outperforms PI control in both a fault-free and faulty environment.