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Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme.

Online linearization-based neural predictive control of air–fuel ratio in SI engines with PID feedback correction scheme.

Authors :
Saraswati, Samir
Chand, Satish
Source :
Neural Computing & Applications. Sep2010, Vol. 19 Issue 6, p919-933. 15p. 7 Diagrams, 11 Graphs.
Publication Year :
2010

Abstract

Model predictive control (MPC) frequently uses online identification to overcome model mismatch. However, repeated online identification does not suit the real-time controller, due to its heavy computational burden. This work presents a computationally efficient constrained MPC scheme using nonlinear prediction and online linearization based on neural models for controlling air–fuel ratio of spark ignition engine to its stoichiometric value. The neural model for AFR identification has been trained offline. The model mismatch is taken care of by incorporating a PID feedback correction scheme. Quadratic programming using active set method has been applied for nonlinear optimization. The control scheme has been tested on mean value engine model simulations. It has been shown that neural predictive control with online linearization using PID feedback correction gives satisfactory performance and also adapts to the change in engine systems very quickly. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09410643
Volume :
19
Issue :
6
Database :
Academic Search Index
Journal :
Neural Computing & Applications
Publication Type :
Academic Journal
Accession number :
52858916
Full Text :
https://doi.org/10.1007/s00521-010-0419-z