Abstract
Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio (i.e., lambda) among all the engine variables. An accurate and adaptive model for lambda prediction is essential to effective lambda control for long term. This paper utilizes an emerging technique, relevance vector machine (RVM), to build a reliable time-dependent lambda model which can be continually updated whenever a sample is added to, or removed from, the estimated lambda model. The paper also presents a new model predictive control (MPC) algorithm for air-ratio regulation based on RVM. This study shows that the accuracy, training, and updating time of the RVM model are superior to the latest modelling methods, such as diagonal recurrent neural network (DRNN) and decremental least-squares support vector machine (DLSSVM). Moreover, the control algorithm has been implemented on a real car to test. Experimental results reveal that the control performance of the proposed relevance vector machine model predictive controller (RVMMPC) is also superior to DRNNMPC, support vector machine-based MPC, and conventional proportional-integral (PI) controller in production cars. Therefore, the proposed RVMMPC is a promising scheme to replace conventional PI controller for engine air-ratio control.
Highlights
Engine power, brake-specific fuel consumption, and emissions relate closely to air ratio among all the engine variables [1]
Superiority, and online update ability of the proposed relevance vector machine (RVM) model, its prediction result was compared with those obtained from the latest methods, decremental least-squares support vector machine (DLSSVM) [18] and diagonal recurrent neural network (DRNN) [8]
MoTeC M800 is mainly used for engine control, whereas National Instrument (NI) USB6259 is used for sending control signal to the MoTeC electronic control unit (ECU) via a LabVIEW interface program according to the MATLAB model predictive control (MPC) program embedded
Summary
Brake-specific fuel consumption, and emissions relate closely to air ratio among all the engine variables [1]. Manzie et al [2, 3] mentioned that if the air-fuel ratio is 1% lower than its stoichiometric ratio (e.g., 14.7 : 1 for gasoline), carbon monoxide (CO) and hydrocarbon (HC) emissions will be significantly increased. An air-fuel ratio that is 1% higher than the stoichiometric ratio produces more nitrogen oxides (NOx), up to 50%. Modern automotive engines are controlled by the electronic control unit (ECU) which usually uses look-up tables with compensation of a proportionalintegral (PI) closed-loop controller for lambda regulation. Since the nature of engine combustion is multivariable, timevarying, time-delay, and chaotic, look-up tables with PI controller cannot produce desirable and accurate lambda control [2, 3]
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