An adaptive predictor corrector strategy for output feedback control of nonlinear hybrid process systems

Abstract

This work presents a methodology for model-based output feedback control of uncertain nonlinear hybrid process systems using an adaptive predictor corrector strategy. A hybrid monitoring scheme is initially developed to facilitate the identification of the active mode at any given time using the measured output. A set of stabilizing output feedback con trollers are then synthesized to robustly stabilize the constituent modes where appropriate state estimators are used. To stabilize each mode with minimal sensor-controller communication, a predictive model of each mode is embedded within the corresponding state feedback controller to provide an estimate of the process state which is used during periods of communication suspension. To determine when the communication must be restored, the evolution of the state estimate for the active mode is monitored and the corresponding state estimator is prompted to send its estimate to update the model state only when some update criteria are satisfied. The key idea is to use the model as a predictor and to use the Lyapunov stability constraint for each mode as a criterion for adaptively correcting the model predictions. The implementation of the proposed methodology is demonstrated using a simulated model of a chemical reactor with multiple operating modes.