The main goal of the raw meal blending control in cement industry is to maintain near the reference moduli values and to decrease the variation of the chemical composition rejecting the disturbances. For this pulpose, Some commercial control systems are available today. But the control structure in these Jystems usually consists of a quality control. In this paper, the proposed constrained self-tuning controller deals with not on& the quality control but quantity control as well. This is accomplished by inserting a energy term into the quadratic peformance index. The self-tuning controller designed according to this performance index bases on a time-series model characterizing relationship between the feedrates and the moduli values. Because of the technological constraints, a optimization problem is formed to compute the control signal. The optimization problem is solved to obtain the least expensive and ifpossible the least sensitive mix of the raw materials that satisjes the quality requirements of the raw meal under incomplete and stochastic information of the different raw material component concentmtions. and further more in real time.

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The design of the autopilot is one of the most important algorithms of missiles. Performance of the autopilot and its robustness are significant matters to hit a target accurately. The autopilot should satisfy the desired performance under disturbances. In the scope of this study, three autopilots were offered for tracking pitch acceleration command using different control methods: three-loop classic control, pole-placement control and receding horizon predictive control. The aim of the autopilot designed by employing receding horizon predictive control is to minimize the flight control effort, and to make the close-loop system insensitive against modelling uncertainties and stochastic shattering factors. This study comes up with that the missile is able to move in desired performance under disturbances such as control surface misplacement, thrust misalignment, wind and aerodynamic uncertainties with more robustness, less control effort and minimum miss distance and terminal time using an alternative control method instead of classic and poleplacement control methods which are generally referred by the defence industry. KeywordsMissile pitch autopilot, three-loop control, pole-placement control, receding horizon predictive control, pitch mathematical model of missile Date

This paper deals with the identification and advanced control of the raw material blending process in cement industry. The process is multivariable and coupled one, because the feeder tanks do not contain homogeneous raw materials chemically. The time delays in the system are also considerable. The disturbances coming from the variations in the chemical compositions of the raw materials from long-term average compositions cause the changes of the system parameters. Therefore, for providing the target values of the oxide compositions of the raw meal determining the high quality of cement, the stochastic multivariable dynamic models are developed and model predictive controllers are designed to calculate the optimal feed ratios of the raw materials despite disturbances. This study consists of two parts; in the identification part, three different linear multivariable stochastic ARX models are proposed. The identification results show that these MISO and MIMO models are good models. In the control part, model predictive control strategy is applied. At the end of the simulation study, the output values reach the specified set points quickly. Also the significant decrease in the variance of controlled outputs is obtained.

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