Quality Model and Artificial Intelligence Base Fuel Ratio Management with Applications to Automotive Engine

Abstract

In this research, manage the Internal Co mbustion (IC) engine modeling and a mu lti-inputmu lti-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. Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, mu lti inputs-multi outputs and time variant. One purpose of accurate modeling is to save development costs of real engines and min imizing the risks of damag ing an engine when validating controller designs. Nevertheless, developing a small model, for specific controller design purposes, can be done and then validated on a larger, mo re complicated model. Analytical dynamic nonlinear modeling of internal co mbustion engine is carried out using elegant Euler-Lag range method compro mising accuracy and complexity. A baseline estimator with varying parameter gain is designed with guaranteed stability to allow imp lementation of the proposed state feedback slid ing mode methodology into a MATLAB simu lation environment, where the sliding mode strategy is imp lemented into a model engine control module ("software"). To estimate the dynamic model of IC engine fu zzy inference engine is applied to baseline sliding mode methodology. The fuzzy inference baseline sliding methodology performance was compared with a well-tuned baseline mu lti-loop PID controller through MATLAB simulat ions and showed improvements, where MATLAB simulat ions were conducted to validate the feasibility of utilizing the developed controller and state estimator for auto motive 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.

Design Intelligent Robust Model-base Sliding Guidance Controller for Spherical Motor

“…By (23) and 21the sliding mode control of spherical motor is calculated as; Figure 3 shows the conventional sliding mode controller for three dimension of spherical motor. [56][57][58][59][60][61][62][63][64][65][66]. Supposed that is the universe of discourse and is the element of , therefore, a crisp set can be defined as a set which consists of different elements will all or no membership in a set.…”

“…Based on foundation of fuzzy logic methodology; fuzzy logic controller has played important rule to design nonlinear controller for nonlinear and uncertain systems [53][54][55][56][57][58][59][60][61][62][63][64][65][66]. However the application area for fuzzy control is really wide, the basic form for all command types of controllers consists of; Design PID like fuzzy controller: the first type of controller is PID like fuzzy logic controller based on 49 rule bases.…”

“…The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input. This in turn would further inspire multi-uncertainties testing for continuum robot [55][56][57][58][59][60][61][62][63][64][65][66]. In project we can use fuzzy logic theory when a plant can be considered as a black box with outputs available for measurement and a possibility of changing inputs.…”

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

“…The remaining terms in the dynamic equations resulting from gravitational potential energies and spring energies are collected in the matrix . The coefficient matrices of the dynamic equations are given below, [58][59][60][61][62][63][64][65][66][67][68][69]. However the application area for fuzzy control is really wide, the basic form for all command types of controllers consists of;…”

“…The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input. This in turn would further inspire multi-uncertainties testing for continuum robot [58][59][60][61][62][63][64][65][66][67][68][69]. In project we can used fuzzy logic theory when a plant can be considered as a black box with outputs available for measurement and a possibility of changing inputs.…”