Cycle-to-cycle feedback control is employed to achieve optimal combustion phasing while maintaining high levels of exhaust gas recirculation by adjusting the spark advance and the exhaust gas recirculation valve position. The control development is based on a control-oriented model that captures the effects of throttle position, exhaust gas recirculation valve position, and spark timing on the combustion phasing. Under the assumption that in-cylinder pressure information is available, an adaptive extended Kalman filter approach is used to estimate the exhaust gas recirculation rate into the intake manifold based on combustion phasing measurements. The estimation algorithm is adaptive since the cycle-to-cycle combustion variability (output covariance) is not known a priori and changes with operating conditions. A linear quadratic regulator controller is designed to maintain optimal combustion phasing while maximizing exhaust gas recirculation levels during load transients coming from throttle tip-in and tip-out commands from the driver. During throttle tip-outs, however, a combination of a high exhaust gas recirculation rate and an overly advanced spark, product of the dynamic response of the system, generates a sequence of misfire events. In this work, an explicit reference governor is used as an add-on scheme to the closed-loop system in order to avoid the violation of the misfire limit. The reference governor is enhanced with model-free learning which enables it to avoid misfires after a learning phase. Experimental results are reported which illustrate the potential of the proposed control strategy for achieving an optimal combustion process during highly diluted conditions for improving fuel efficiency.
We apply retrospective cost adaptive control (RCAC) to command-following and disturbance-rejection problems for a diesel engine model. The engine is a multi-input, multi-output system with strong static and dynamic interactions, nonlinearities, uncertainties and nonminimum phase characteristics. We demonstrate that RCAC is effective for both the linearized and nonlinear engine models provided that two Markov parameters of the linearized engine plant model are known, either analytically or through system identification. For the commandfollowing and disturbance-rejection problems, we consider the case when the disturbance is harmonic but otherwise unknown, and while the command signal is harmonic and known but no advance knowledge of its spectrum is assumed to be available.
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