Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller
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...
Main Authors: | Pak Kin Wong, Hang Cheong Wong, Chi Man Vong, Tong Meng Iong, Ka In Wong, Xianghui Gao |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2015-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2015/317142 |
Similar Items
-
Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine
by: Hang-cheong Wong, et al.
Published: (2012-01-01) -
Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation
by: Pak Kin Wong, et al.
Published: (2014-01-01) -
Simultaneous-Fault Diagnosis of Automotive Engine Ignition Systems Using Prior Domain Knowledge and Relevance Vector Machine
by: Chi-Man Vong, et al.
Published: (2013-01-01) -
Design and experimental evaluation of predictive engine air-ratio control using relevance vector machine
by: Wong, Hang Cheong
Published: (2009) -
Fault and Noise Tolerance in the Incremental Extreme Learning Machine
by: Ho Chun Leung, et al.
Published: (2019-01-01)