Citation
Takashi Nagata, Masayoshi Tomizuka. "Robust Engine Torque Control by Iterative Learning Control". Proceedings of the 2009 American Control Conference ACC09, St. Louis, Missouri, 2064-2069, 11, June, 2009.

Abstract
Fast-response engine torque control is robustly realized under repetitive air throttle input. An application of iterative learning control (ILC) to robustify the performance of disturbance observer (DOB) is proposed and numerically evaluated. The proposed scheme detects dynamical model discrepancy between an actual engine and its nominal model, and compensate for it to realize nominal plant dynamics. With the applied ILC realizing improved detection of model discrepancy, the scheme is significantly more effective than a conventional DOB under practical test-bench conditions such as measurement delays, noise, and insufficient data measurements.

Electronic downloads

• http://dx.doi.org/10.1109/ACC.2009.5159841

• HTML

  Takashi Nagata, Masayoshi Tomizuka. <a
  href="http://chess.eecs.berkeley.edu/pubs/613.html"
  >Robust Engine Torque Control by Iterative Learning
  Control</a>, Proceedings of the 2009 American Control
  Conference ACC09, St. Louis, Missouri, 2064-2069, 11, June,
  2009.

• Plain text

  Takashi Nagata, Masayoshi Tomizuka. "Robust Engine
  Torque Control by Iterative Learning Control".
  Proceedings of the 2009 American Control Conference ACC09,
  St. Louis, Missouri, 2064-2069, 11, June, 2009.

• BibTeX

  @inproceedings{NagataTomizuka09_RobustEngineTorqueControlByIterativeLearningControl,
      author = {Takashi Nagata and Masayoshi Tomizuka},
      title = {Robust Engine Torque Control by Iterative Learning
                Control},
      booktitle = {Proceedings of the 2009 American Control
                Conference ACC09, St. Louis, Missouri},
      pages = {2064-2069},
      day = {11},
      month = {June},
      year = {2009},
      abstract = {Fast-response engine torque control is robustly
                realized under repetitive air throttle input. An
                application of iterative learning control (ILC) to
                robustify the performance of disturbance observer
                (DOB) is proposed and numerically evaluated. The
                proposed scheme detects dynamical model
                discrepancy between an actual engine and its
                nominal model, and compensate for it to realize
                nominal plant dynamics. With the applied ILC
                realizing improved detection of model discrepancy,
                the scheme is significantly more effective than a
                conventional DOB under practical test-bench
                conditions such as measurement delays, noise, and
                insufficient data measurements.},
      URL = {http://chess.eecs.berkeley.edu/pubs/613.html}
  }
  

Posted by Christopher Brooks on 21 Aug 2009.
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