I. IntroductionThe internal co mbustion (IC) engine is designed to produce power fro m the energy that is contained in its fuel. More specifically, its fuel contains chemical energy and together with air, this mixtu re is burned to output mechanical power. There are various types of fuels which can be used in IC engines namely; petroleum, d iesel, bio-fuels, and hydrogen [1]. Modeling of an entire IC engine is a very impo rtant and complicated process because engines are nonlinear, multi inputs-multi outputs (MIMO) and time variant.Controller design is the main parts in this paper as well as the majo r objectives in the controller design are stability and robustness. One of the significant challenges in control algorith ms is design a linear controller fo r nonlinear systems. When system works with various parameters and hard nonlinearit ies this technique is very useful in order to be implemented easily but it has some limitations such as working near the system operating point [9]. So me of IC engines which work in industrial processes are controlled by linear controllers, but linear controller design for IC engines is extremely difficu lt [1][2][3][4][5][6]. Co mputed torque controller (CTC) is a powerfu l nonlinear controller which it widely used in control of IC engine. It is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. This controller works very well when all dynamic and physical parameters are known but when the IC engine has variation in dynamic parameters, in this situation the controller has no acceptable performance [7][8][9][10][11][12][13][14]. In practice, most of physical systems (e.g., IC engine) parameters are unknown o r t ime variant, therefore, online tuneable gain co mputed torque controller used to compensate dynamic equation of IC engine [1,6]. Sliding mode controller (SMC) is one of the influential nonlinear controllers in certain and uncertain systems which are used to solved stability and robustness [10]. The main reason for this popularity is the attractive properties which SMCs have, such as good control performance for nonlinear systems, applicability to MIMO systems and well-established design criteria for discrete-time systems. SMC may emp loy unnecessarily large control signals to overcome the parametric uncertainties and difficulty in the calculat ion of what is known as the equivalent control [11][12][13][14][15][16][17]. In various dynamic parameters systems that need to be training online adaptive control methodology is used. Adaptive control methodology can be classified into two main groups, namely, t raditional adaptive method and fuzzy adaptive method [18][19][20][21][22]. Fu zzy adaptive method is used in systems which want to train ing parameters by expert knowledge. Trad itional adaptive method is used in systems which some dynamic parameters are known. In this research in order to solve disturbance rejection and uncertainty dynamic parameter, adaptive method are applied to slid ing mode controller and c...
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.
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
<p class="Default">In this research, manage the Internal Combustion (IC) engine modeling and a multi-input-multi-output artificial intelligence baseline chattering free sliding mode methodology scheme is developed with guaranteed stability to simultaneously control fuel ratios to desired levels under various air flow disturbances by regulating the mass flow rates of engine PFI and DI injection systems. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, more complicated model. Analytical dynamic nonlinear modeling of internal combustion engine is carried out using elegant Euler-Lagrange method compromising accuracy and complexity. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline multi-loop PID controller through MATLAB simulations and showed improvements, where MATLAB simulations were conducted to validate the feasibility of utilizing the developed controller and state estimator for automotive engines. The proposed tracking method is designed to optimally track the desired FR by minimizing the error between the trapped in-cylinder mass and the product of the desired FR and fuel mass over a given time interval.</p>
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