Combustion Control of Diesel Engine using Feedback Error Learning with Kernel Online Learning Approach

Elfady Satya Widayaka, Hiromitsu Ohmori

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

This paper shows how to design Multivariable Model Reference Adaptive Control System (MRACS) for "Tokyo University discrete-time engine model" proposed by Yasuda et al (2014). This controller configuration has the structure of "Feedback error learning (FEL)" and adaptive law is based on kernel method. Simulation results indicate that "kernelized" adaptive controllers can improve the tracking performance, the speed of convergence and the robustness to disturbances.

Original languageEnglish
Article number012107
JournalJournal of Physics: Conference Series
Volume744
Issue number1
DOIs
Publication statusPublished - 2016 Oct 3
Event13th International Conference on Motion and Vibration Control, MOVIC 2016 and the 12th International Conference on Recent Advances in Structural Dynamics, RASD 2016 - Southampton, United Kingdom
Duration: 2016 Jul 42016 Jul 6

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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