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Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines

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Abstract

In this paper, a fuzzy model predictive controller is developed to reduce the emission pollutants in spark ignition internal combustion engines. The path to this control goal is regulating the amount of normalized air-to-fuel ratio in the engine. In order to generate the simulation data, mean value engine model is simulated. To approximate the nonlinear and fast time-varying dynamics of the engine, a modified fuzzy relational model is trained offline in batch mode. For training, gradient descent back propagation algorithm along with evolutionary asexual reproduction optimization algorithm is used. Nonlinear structure of the fuzzy model of the engine imposes nonlinear optimization to produce control signals. Hence, gradient descent algorithm is used to generate online control signals. The effectiveness and robustness of the controller are evaluated through simulations.

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Notes

  1. 1.

    Note that R and \({\mathscr {K}}\) are constants.

  2. 2.

    Pulse-width modulation.

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Correspondence to Tohid Sardarmehni.

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The proposed study in the manuscript involved no experiment on any live existence such as humans or animals.

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Sardarmehni, T., Aghili Ashtiani, A. & Menhaj, M.B. Fuzzy model predictive control of normalized air-to-fuel ratio in internal combustion engines. Soft Comput 23, 6169–6182 (2019) doi:10.1007/s00500-018-3270-2

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Keywords

  • Fuzzy modeling
  • Model predictive control
  • Engine control
  • Optimization