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Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine

  • Extreme Learning Machine and Deep Learning Networks
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Abstract

The automotive engine idle speed control problem is a compromise among low engine speed for fuel saving, minimum emissions and disturbance rejection ability to prevent engine stall. However, idle speed regulation is very challenging due to the presence of high nonlinearity and aging-caused uncertainties in the engine dynamics. Therefore, the engine idle speed system is a typical uncertain nonlinear system. To address the problems of inherent nonlinearity and uncertainties in idle speed regulation, an extreme learning machine (ELM)-based adaptive neural control algorithm is proposed for tracking the target idle speed adaptively. The purpose of ELM is to rapidly deal with the uncertain nonlinear engine system. Since the original ELM is not designed for adaptive control, a new adaptation law is designed to update the weights of ELM in the sense of Lyapunov stability. Experiment is conducted to validate the performance of the proposed control method. Experimental result indicates that the ELM-based adaptive neural control outperforms the classical proportional–integral–derivative (PID), fuzzy-PID and back-propagation-neural-network-based controllers in terms of tracking performance under the variation of engine load.

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Acknowledgements

This research is supported by the research grants of the University of Macau under grant numbers MYRG2016-00212-FST, MYRG2017-00135-FST and MYRG2019-00028-FST. The authors would like to thank Dr. Ka In Wong, Mr. Xiaoli Zhang and Mr. Tianyi Zhang for their assistance.

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  1. Pak Kin Wong and Wei Huang have contributed equally to this work as co-first authors.

Authors and Affiliations

  1. Department of Electromechanical Engineering, University of Macau, Taipa, Macau

    Pak Kin Wong, Wei Huang & Zhixin Yang

  2. Department of Computer and Information Science, University of Macau, Taipa, Macau

    Chi Man Vong

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  1. Pak Kin Wong
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  2. Wei Huang
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Correspondence to Pak Kin Wong.

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Wong, P.K., Huang, W., Vong, C.M. et al. Adaptive neural tracking control for automotive engine idle speed regulation using extreme learning machine. Neural Comput & Applic 32, 14399–14409 (2020). https://doi.org/10.1007/s00521-019-04482-5

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