Approximate Nonlinear Model Predictive Control For Gasoline Engines
Raechel Tan, Chung-Yen Lin, Masayoshi Tomizuka

Citation
Raechel Tan, Chung-Yen Lin, Masayoshi Tomizuka. "Approximate Nonlinear Model Predictive Control For Gasoline Engines". Talk or presentation, 12, February, 2015; Poster presented at the 2015 Berkeley EECS Annual Research Symposium.

Abstract
An approximate nonlinear model predictive control (NMPC) is presented for use on a gasoline engine with exhaust gas recirculation (EGR). The control task is to minimize fuel consumption and torque tracking error, while also avoiding knock and misfire. In the NMPC framework, a nonlinear dynamic model of the engine is used to train a state feedback controller, while also considering the constraints. The optimal controller is approximated by a look-up table that is fast to compute in real time. An unscented Kalman filter is used for state estimation. Implentation on a benchmark engine simulator shows a significant performance improvement over the baseline controller.

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Citation formats

• HTML

  Raechel Tan, Chung-Yen Lin, Masayoshi Tomizuka. <a
  href="http://chess.eecs.berkeley.edu/pubs/1087.html"><i>Approximate
  Nonlinear Model Predictive Control For Gasoline
  Engines</i></a>, Talk or presentation,  12,
  February, 2015; Poster presented at the <a             
  href="http://www.terraswarm.org/conferences/15/bears/"
  >2015 Berkeley EECS Annual Research Symposium</a>.

• Plain text

  Raechel Tan, Chung-Yen Lin, Masayoshi Tomizuka.
  "Approximate Nonlinear Model Predictive Control For
  Gasoline Engines". Talk or presentation,  12, February,
  2015; Poster presented at the <a             
  href="http://www.terraswarm.org/conferences/15/bears/">2015
  Berkeley EECS Annual Research Symposium</a>.

• BibTeX

  @presentation{TanLinTomizuka15_ApproximateNonlinearModelPredictiveControlForGasoline,
      author = {Raechel Tan and Chung-Yen Lin and Masayoshi
                Tomizuka},
      title = {Approximate Nonlinear Model Predictive Control For
                Gasoline Engines},
      day = {12},
      month = {February},
      year = {2015},
      note = {Poster presented at the <a             
                href="http://www.terraswarm.org/conferences/15/bears/">2015
                Berkeley EECS Annual Research Symposium</a>},
      abstract = {An approximate nonlinear model predictive control
                (NMPC) is presented for use on a gasoline engine
                with exhaust gas recirculation (EGR). The control
                task is to minimize fuel consumption and torque
                tracking error, while also avoiding knock and
                misfire. In the NMPC framework, a nonlinear
                dynamic model of the engine is used to train a
                state feedback controller, while also considering
                the constraints. The optimal controller is
                approximated by a look-up table that is fast to
                compute in real time. An unscented Kalman filter
                is used for state estimation. Implentation on a
                benchmark engine simulator shows a significant
                performance improvement over the baseline
                controller.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1087.html}
  }
  

Posted by Raechel Tan on 13 Feb 2015.
Groups: chess
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