Internal co mbustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. Closed loop combustion control is a key technology that is used to optimize the engine combustion process to achieve this goal. In order to conduct research in the area of closed loop combustion control, a control oriented cycle-to-cycle engine model, containing engine combustion information for each individual engine cycle as a function of engine crank angle, is a necessity. In this research, the IC engine is modeled accord ing to fuel ratio, wh ich is represented by the mass of air. In this research, a mult i-input-mu ltioutput baseline co mputed fuel control scheme is used to simu ltaneously control the mass flow rate of both port fuel injection (PFI) and direct in jection (DI) systems to regulate the fuel ratio of PFI to DI to desired levels. The control target is to maintain the fuel ratio at stoichiometry and the fuel ratio to a desired value between zero and one. The performance of the baseline computed fuel controller is co mpared with that of a baseline proportional, integral, and derivative (PID) controller.

Abstract-Both fuzzy logic and computed fuel rat io can compensate the steady-state error o f proportionalderivative (PD) method. This paper presents parallel computed fuel rat io co mpensation for fu zzy plus PID control management with application to internal combustion (IC) engine. The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven. For the parallel structure, the fin ite time convergence with a super-twisting second-order slidingmode is guaranteed.

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In this research, model reference fuzzy based control is presented as robust controls for IC engine. The objective of the study is to design controls for IC engines without the knowledge of the boundary of uncertainties and dynamic information by using fuzzy model reference PD plus mass of air while improve the robustness of the PD plus mass of air control. A PD plus mass of air provides for eliminate the mass of air and ultimate accuracy in the presence of the bounded disturbance/uncertainties, although this methods also causes some oscillation. The fuzzy PD plus mass of air is proposed as a solution to the problems crated by unstability. This method has a good performance in presence of uncertainty