Feedback error learning control using OS-ELM for SI engine airpath systems

Dongyang Wang, Yuhki Hashimoto, Hiromitsu Ohmori

Research output: Contribution to journalConference articlepeer-review


Currently, due to the stricter emission legislation, spark-ignition (SI) engine with low pressure exhaust gas recirculation (LP-EGR) has become a research hotspot. However, with the increasing complexity of engine, it is difficult to obtain sufficient control performance by conventional MAP control or PID control. This paper focuses on the airpath system of a SI engine with low pressure EGR, proposes a new model-based control method for the model of the airpath system based on the Feedback Error Learning control using online sequential-extreme learning machine (OS-ELM) as controller algorithm. In this paper, the airpath model has been built and effectiveness of the proposed control method has been confirmed on the model.

Original languageEnglish
Pages (from-to)182-188
Number of pages7
Issue number10
Publication statusPublished - 2021
Event6th IFAC Conference on Engine Powertrain Control, Simulation and Modeling E-COSM 2021 - Tokyo, Japan
Duration: 2021 Aug 232021 Aug 25


  • Adaptive learning control
  • Air path system
  • EGR
  • Feedback error learning
  • OS-ELM
  • SI engine

ASJC Scopus subject areas

  • Control and Systems Engineering


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