Probabilistic Guaranteed Gradient Learning-Based Spark Advance Self-Optimizing Control for Spark-Ignited Engines

In spark-ignited (SI) engines, the spark advance (SA) controls the combustion phase that has a significant impact on the efficiency. Online self-optimizing control (SOC) of SA to maximize the indicated fuel conversion efficiency (IFCE) forms a stochastic optimization problem for a static map due to...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2018-10, Vol.29 (10), p.4683-4693
Hauptverfasser:	Zhang, Yahui, Gao, Jinwu, Shen, Tielong
Format:	Artikel
Sprache:	eng
Schlagworte:	Automobile industry Combustion Combustion control Decision analysis Dithers Energy conversion efficiency Engine tests Engines Fuels Internet Learning Online gradient learning Optimization Probabilistic logic Probability distribution Probability theory self-optimizing control (SOC) Silicon spark advance (SA) Spark ignition spark-ignited (SI) engine Statistical analysis Statistical distributions Statistical methods stochastic optimization Stochasticity
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