Description Nomenclature Chemical NamesEthinyloestradiol-3-methyl ether. 1 7 a-Ethnylestradiol-3-methyl ether 3-Plethoxy-19-nor-17 a-pregna-1,3,5(10)-trien-20yn-17-01. 19-Norpregna-1,3,5(1O)-trien-2O-yn-l7-ol,3methoxy-(17a)-17a-Ethynyl-3-methoxy-1,3,5(lO)-estratrien-l7~-01. 17~-Ethynyl-3-methoxyoestra-ly3,5(lO)-trien-l7-ol. 17~t-Ethynyl-1,3,5(10)-estratriene-3, 17B-diol-3methyl ether.

The paper deals with the problem of modelling and control using the Local Model Network (LMN). The idea is based on development of multiple local models for the whole operating range of the controlled process. The local models are then smoothly connected using the validity or weighting functions to provide a nonlinear global model of the plant. For saving the computational load, linear model is obtained by interpolating the parameters of local models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behaviour of the process. The supervisory program, based on a nonlinear global model, computes desired values of manipulated variables leading to minimum utility consumption. The approach is verified in a control of model of a heat exchanger.

The paper deals with modelling of a magnetic levitation laboratory plant. The goal of the work was to create a nonlinear model in a MATLAB / Simulink environment representing behaviour of a real-time CE152 laboratory plant. The CE152 is a magnetic levitation model developed by Humusoft company. From the control point of view, the CE152 magnetic levitation plant is a nonlinear very fast system. The model of the plant is developed using first principle modelling and subsequently made more precise using real-time experiments. The behaviour of the resulting Simulink model is compared with the behaviour real-time plant. The Simulink model can be further used in the process of controller design.

This paper deals with modeling and control of a hydraulic three tank system. A process of creating a computer model in MATLAB / Simulink environment is described and optimal PID and model predictive controllers are proposed. Modeling starts with creation of an initial mathematical model based on first principles approach. Further, the initial model is modified to obtain better correspondence with real-time system and parameters of the modified system are identified from measurements. The real time system contains nonlinearities which cannot be neglected and therefore are identified and included in the final mathematical model. Resulting model is used for control design. As the real-time system has long time constants, usage of Simulink model dramatically speeds up design process. Optimal PID and MPC controllers are proposed and compared. Described techniques are not limited to one particular modeling problem but can be used as an illustrative example for modeling of many technological processes.

One of the most widespread modern control strategies is the discrete-time Model Predictive Control (MPC) method which requires the solution of the quadratic programming problem. For systems with binary input variables the quadratic problem is replaced by more challenging Mixed-Integer Quadratic Programming (MIQP) problem. The objective of this work is the implementation of MIQP problem solver in a low power embedded computing platform with limited computational power and limited memory. The MIQP problem is solved using branch-and-bound method and the solution of the relaxed original quadratic problems with equality and inequality constraints solved in the nodes of a binary tree is found with interior-point algorithm. A simulation study of the reserve constrained economic dispatch problem for power generators with prohibited zones is presented. Simulation results show the applicability of the proposed solver for small size MIQP problems.