The output feedback control problem for a class of nonlinear distributed parameter systems with limited number of continuous measurement sensors that describes a wide range of physico-chemical systems is investigated using adaptive proper orthogonal decomposition (APOD) method. Specifically, APOD is used to initiate and recursively revise locally valid reduced order models (ROMs) that approximate the dominant dynamic behavior of such physico-chemical systems. The controller is designed based on ROMs by combining a robust state controller with an APOD-based nonlinear Luenberger-type switching dynamic observer of the system states to reduce measurement sensor requirements. The important static observer requirements on the number of measurement sensors (that they must be supernumerary to the ROM dimension) and their location are circumvented by synthesizing dynamic observers. Three different approaches are introduced to recursively compute the dynamic observer gains at the ROM revisions. The stability of the closed-loop system is proven via Lyapunov and hybrid system stability arguments without invoking the separation principle between control and observation. The proposed method is successfully used to regulate a physico-chemical system that can be described in the form of the Kuramoto–Sivashinsky equation when the process exhibits significant nonlinear behavior.
Bibliographical noteFunding Information:
Financial support from the National Science Foundation, CMMI Award # 13-00322 is gratefully acknowledged.
© 2015 Elsevier Ltd
Copyright 2020 Elsevier B.V., All rights reserved.
- Adaptive model reduction
- Distributed parameter systems
- Dynamic observer
- Nonlinear systems
- Output feedback control
- Process control