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Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

机译：基于极限学习机模型预测控制器的容错汽车空气比控制

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摘要

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM), to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.
机译：需要有效的空气比控制以保持最佳的发动机性能。但是，传统的空气比率控制假定位于尾管的lambda传感器正常工作，并且强烈依赖于由lambda传感器测量的空气比率反馈信号。当传感器在冷启动过程中或发生故障时正在预热时，传统的空气比例控制将不再起作用。为了解决这个问题，本文采用了一种先进的建模技术，即内核极限学习机（ELM），以建立备用的空气比率模型。根据模型的预测，即使λ传感器不工作，也可以维持有限的空气比控制性能。这样的策略被实现为容错控制。为了验证所提出的容错空气比例控制策略的有效性，基于内核ELM备用空气比例模型构造了模型预测控制方案，并在实际发动机上实现了该模型。实验结果表明，所提出的控制器可以在外部干扰和不存在来自λ传感器的空气比反馈信号的情况下，将空气比调节至特定目标值，且具有令人满意的公差。这意味着所提出的容错空气比例控制是一种有前途的方案，可以在λ传感器出现故障或预热时保持空气比例控制性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第9期|317142.1-317142.10|共10页
  • 作者

    Wong Pak Kin; Wong Hang Cheong; Vong Chi Man; Iong Tong Meng; Wong Ka In; Gao Xianghui;

  • 作者单位

    Univ Macau, Dept Elect Engn, Macau, Peoples R China.;

    Univ Macau, Dept Elect Engn, Macau, Peoples R China.;

    Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China.;

    Univ Macau, Dept Elect Engn, Macau, Peoples R China.;

    Univ Macau, Dept Elect Engn, Macau, Peoples R China.;

    Univ Macau, Dept Elect Engn, Macau, Peoples R China.;

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