Sharif Digital Repository

Estimation of major turbocharger variables is essential for proper control and monitoring of a turbocharged engine. This work presents a novel algorithm to estimate turbocharger rotator speed, air temperature downstream a compressor and flow rate over the compressor using mean value models of engine subsystems. A nonlinear Luenberger observer is designed for a 1.7-lit gasoline turbocharged engine. The designed observed is shown analytically to be asymptotically stable. Performance of the designed observer is experimentally validated with the data collected from the engine. The results indicate the observer can capture major turbocharger's rotational dynamics and estimated turbocharger... 

Estimation of major turbocharger variables is essential for proper control and monitoring of a turbocharged engine. This work presents a novel algorithm to estimate turbocharger rotator speed, air temperature downstream a compressor and flow rate over the compressor using mean value models of engine subsystems. A nonlinear Luenberger observer is designed for a 1.7-lit gasoline turbocharged engine. The designed observed is shown analytically to be asymptotically stable. Performance of the designed observer is experimentally validated with the data collected from the engine. The results indicate the observer can capture major turbocharger's rotational dynamics and estimated turbocharger... 

Recently, many researchers and practitioners have shown interest on profile monitoring as a relatively new subarea of statistical process control. One main reason for this interest, and perhaps a key factor for the contributions of many researchers to this field, is the various applications of profile monitoring in real life. Although one can easily encounter many univariate applications of profile monitoring in service and manufacturing environments, there exist situations where quality of a product or process needs to be modeled in multivariate terms. In this paper, we investigate monitoring of multivariate simple linear profiles in phase II when independence assumption of observations... 

Monitoring and controlling the depth of anesthesia is really important, since over dosing and under dosing can be dangerous for the patients. Pharmacokinetic-Pharmacodynamic models vastly used for describing the relationship between input anesthetic agents and output patient endpoint variables. As there is a large variety between the patients so for controlling the depth of anesthesia we need a controller which should be robust enough and also because the anesthesia process is nonlinear and contains time delay, among them all the proposed methods for controlling the depth of anesthesia, model predictive controllers (MPCs) are good choices. Extended dynamic matrix control (EDMC) can be...