  Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine
combustion timing predictions must contend with non-linear chemistry,
non-linear physics, period doubling bifurcation(s), turbulent mixing, model
parameters that can drift day-to-day, and air-fuel mixture state information
that cannot typically be resolved on a cycle-to-cycle basis, especially during
transients. In previous work, an abstract cycle-to-cycle mapping function
coupled with $\epsilon$-Support Vector Regression was shown to predict
experimentally observed cycle-to-cycle combustion timing over a wide range of
engine conditions, despite some of the aforementioned difficulties. The main
limitation of the previous approach was that a partially acausual randomly
sampled training dataset was used to train proof of concept offline
predictions. The objective of this paper is to address this limitation by
proposing a new online adaptive Extreme Learning Machine (ELM) extension named
Weighted Ring-ELM. This extension enables fully causal combustion timing
predictions at randomly chosen engine set points, and is shown to achieve
results that are as good as or better than the previous offline method. The
broader objective of this approach is to enable a new class of real-time model
predictive control strategies for high variability HCCI and, ultimately, to
bring HCCI's low engine-out NOx and reduced CO2 emissions to production
engines.
