Stability analysis of nonlinear networked control systems using model predictive control scheme

Abdul Wahid A. Saif*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Model predictive control (MPC) has received much attention in the past decades due to its extensive applications in the control of industrial processes such as distillation and fractionation, pulp and paper processing. It works on the principle of receding horizon control, where only the first change in each independent variable u(k|k) is implemented, and the calculation is repeated when the next change is required. This feature makes the MPC approach very appropriate to incorporate the input/output constraints into the on-line optimization as well as to compensate time delays, which increases the possibility of its application in the synthesis and analysis of Networked Control Systems (NCS). In this work, an observer based MPC will be applied to Nonlinear Networked Control Systems (NNCS). Due to space limit, the NNCS problem is formulated first, then only the theory of stability is developed and reported. In the next work, the controller and the observer gains will be established. The results will be tested with simulation.

Original languageEnglish
Title of host publication2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-352
Number of pages7
ISBN (Electronic)9781538653050
DOIs
StatePublished - 7 Dec 2018

Publication series

Name2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Control and Optimization
  • Instrumentation

Fingerprint

Dive into the research topics of 'Stability analysis of nonlinear networked control systems using model predictive control scheme'. Together they form a unique fingerprint.

Cite this