Stabilization of nonlinear processes with input constraints

Navneet Kapoor, Prodromos Daoutidis

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

This work deals with nonlinear processes which, in the absence of input constraints, can be locally stabilized by a linearizing static state feedback law. Such systems, under unconstrained linearizing control laws, possess a cascaded structure between a (asymptotically or exponentially) stable nonlinear subsystem and an exponentially stable linear subsystem, which allows for a straightforward stability analysis. In the presence of input constraints, however, this cascaded structure breaks down to an interconnection between two nonlinear subsystems; analyzing the stability of such interconnections is a rather cumbersome task, that typically results in conservative estimates of regions of stability. In this article, we present an analysis framework for the local stabilization of such processes and the estimation of regions of closed-loop stability in the presence of input constraints. The proposed approach entails: (i) specifying a region in state- space where the closed-loop system behaves effectively as a cascade and asymptotic stability can be guaranteed in the presence of constraints, provided that the states of the system remain in this region for all times; and (ii) constructing invariant sets within this region that qualify as regions of closed-loop stability. A detailed case study is carried out on a polymerization reactor example and the desirable features of the proposed methodology are aptly illustrated. (C) 2000 Elsevier Science Ltd.

Original languageEnglish (US)
Pages (from-to)9-21
Number of pages13
JournalComputers and Chemical Engineering
Volume24
Issue number1
DOIs
StatePublished - Apr 3 2000

Bibliographical note

Funding Information:
Financial support from the National Science Foundation, CTS-9624725 is gratefully acknowledged.

Fingerprint Dive into the research topics of 'Stabilization of nonlinear processes with input constraints'. Together they form a unique fingerprint.

Cite this