Stochastic Nonlinear Model Predictive Control with Active Model Discrimination: A Closed-Loop Fault Diagnosis Application

Abstract

This paper addresses the problem of model predictive control with multiple models for nonlinear systems subject to stochastic disturbances. The multiple models can represent various operating conditions such as system faults or failures, or arise from model structure uncertainty. The paper presents a stochastic nonlinear model predictive control (SNMPC) approach with endogenous learning for active discrimination between the competing models based on closed-loop system observations. The system learning is endogenized through explicit inclusion of a model discrimination measure into the stochastic optimal control problem, which facilitates probabilistic discrimination between the predictions of multiple models. The control approach uses a Bayesian estimation algorithm for recursive estimation of the probabilities that represent the degree to which each model predicts the online system observations. The performance of the proposed SNMPC approach with active model discrimination is demonstrated for closed-loop fault diagnosis.