Fangwei Xu

Dynamic output feedback robust model predictive control

The synthesis approach for dynamic output feedback robust model predictive control is considered. The notion of quadratic boundedness is utilised to characterise the stability properties of the augmented closed-loop system. A finite horizon performance cost, which corresponds to the worst case of both the polytopic uncertainty and the bounded disturbance/noise, is utilised. It is not required to specify the horizon length. A numerical example is given to illustrate the effectiveness of the proposed controller.

ASSESSMENT OF ECONOMIC PERFORMANCE OF MODEL PREDICTIVE CONTROL THROUGH VARIANCE/CONSTRAINT TUNING

Abstract Multivariate controller performance assessment (MVPA) has been developed over the last several years, but its application in advanced model predictive control (MPC) has been very limited mainly due to issues associated with comparability of variance control objective and that of MPC. MPC has been proven as one of the most effective advanced process control (APC) strategies to deal with multivariable constrained control problems with an ultimate objective towards economic optimization. Any attempt to evaluate MPC performance should therefore consider constraints and economic performance. This work is to establish a link between variance control and MPC in terms of economic performance. We show that the variance based performance assessment may be transfered to economic assessment of MPC. Algorithms for economic performance assessment and tuning are developed through linear matrix inequalities using routine operating process data. The proposed algorithms are illustrated via an industrial MPC application example.

Multiple Model Approach for Inferential Instruments Design

Abstract In this paper, an application of a multiple model approach for the design of inferential instruments is reported. The multiple model of interest presents a decoupled structure in the sense that the sub-models do not share the same state variable. A two-stage identification procedure is developed for the model identification and a soft sensor application is later conducted with the decoupled multiple model structure. The soft sensor aims at predicting a quality variable for an industrial separation unit. The decoupled multiple model structure allows obtaining a dynamical model for the soft sensor despite the presence of practical constraints related to multi-rate sampling problem. Real-time implementation results are presented.