TY - GEN
T1 - Adaptive air-fuel ratio control for spark ignition engines with time-varying parameter estimation
AU - Chen, Anthony Siming
AU - Na, Jing
AU - Herrmann, Guido
AU - Burke, Richard
AU - Brace, Chris
PY - 2018/5
Y1 - 2018/5
N2 - This paper presents a novel adaptive controller of air-fuel ratio (AFR) in spark ignition (SI) engines. The controller robustly estimates unknown time-varying engine parameters and thus improves both the transient and steady-state performance. The objective is to regulate the AFR in the combustion chamber around the stoichiometric value by manipulating the injected fuel mass flow rate so as to improve fuel economy and to reduce emissions. The AFR regulation problem is first reformulated into a tracking control problem of the fuel mass flow. This simplifies the control synthesis, i.e. the number of parameters to be online updated can be reduced. Then a representation of the parameter estimation error is derived by using auxiliary filter operations, and used as a new leakage term in the adaptive law. In this case, exponential convergence of the AFR error and the estimation of the time-varying parameters can be proved simultaneously. The proposed controller is compared with a generic adaptive controller using the gradient descent method based on a well-calibrated mean value engine model (MVEM). Finally, the proposed controller is also validated with a commercial engine simulation software, GT-Power, demonstrating better results than for the gradient descent approach.
AB - This paper presents a novel adaptive controller of air-fuel ratio (AFR) in spark ignition (SI) engines. The controller robustly estimates unknown time-varying engine parameters and thus improves both the transient and steady-state performance. The objective is to regulate the AFR in the combustion chamber around the stoichiometric value by manipulating the injected fuel mass flow rate so as to improve fuel economy and to reduce emissions. The AFR regulation problem is first reformulated into a tracking control problem of the fuel mass flow. This simplifies the control synthesis, i.e. the number of parameters to be online updated can be reduced. Then a representation of the parameter estimation error is derived by using auxiliary filter operations, and used as a new leakage term in the adaptive law. In this case, exponential convergence of the AFR error and the estimation of the time-varying parameters can be proved simultaneously. The proposed controller is compared with a generic adaptive controller using the gradient descent method based on a well-calibrated mean value engine model (MVEM). Finally, the proposed controller is also validated with a commercial engine simulation software, GT-Power, demonstrating better results than for the gradient descent approach.
KW - Air-fuel ratio control
KW - spark ignition engines
KW - adaptive control
KW - parameter estimation
KW - GT-Power simulation
U2 - 10.1109/ICMIC.2017.8321616
DO - 10.1109/ICMIC.2017.8321616
M3 - Conference contribution
SN - 9781509065769
SP - 1074
EP - 1079
BT - 2017 9th International Conference on Modelling, Identification and Control (ICMIC 2017)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -