Sharif Digital Repository

This paper presents a control oriented model for predicting turbine major variables in a turbocharged spark ignition engine. The turbine is simulated as a two-nozzle chamber where the pressure ratio over the two nozzles is not the same. A convex nonlinear estimation algorithm is formulated to determine the relation between these pressure ratios. The new model is experimentally validated with transient and steady state data collected from a 1.7 liter gasoline engine. The results show the new model can predict the turbine mass flow with an average error of 1.4%. In addition, the application of the turbine model is illustrated for the design of a nonlinear observer to estimate the turbocharger... 

A new algorithm for complete pre-flight calibration of triple magnetometers is developed. The traditional approach for calibrating these sensors are based on a cumbersome procedure called 'swing' that involves levelling and rotating the vehicle containing the magnetometers through a series of known headings. Application of such a procedure is difficult and costly. Recently, new approaches have been developed to calibrate magnetometers without the need of attitude information. Such methods are used mostly for the calibration of biases and scale factors. Additionally in situations where misalignment errors are also to be estimated, they are usually modelled as errors of a non-orthogonal frame... 

A new heuristic controller, called Continuous Ant Colony Controller, is proposed for nonlinear stochastic systems. The new controller formulates the states estimation and model predictive control problems as a single stochastic dynamic optimization problem and utilizes a colony of virtual ants to find and track the best state estimation and the best control signal. For this purpose an augmented state space is defined. An integrated cost function is also defined to evaluate the ants within the state space. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants search the augmented state space dynamically in a similar... 

A new heuristic controller, called the continuous ant colony controller, is proposed for non-linear stochastic Gaussian/non-Gaussian systems. The new controller formulates the state estimation and the model predictive control problems as a single stochastic dynamic optimization problem, and utilizes a colony of virtual ants to find and track the best estimated state and the best control signal. For this purpose, an augmented state space is defined. An integrated cost function is also defined to evaluate the points of the augmented state space, explored by the ants. This function minimizes simultaneously the state estimation error, tracking error, control effort and control smoothness. Ants...