Abstract
ABSTRACTThe expected utility theory is the approach to measurement and utilization of qualitative, conceptual information. The subject of this paper is the design of methodology and algorithms for evaluation of expert utility that permit development of value-driven decision support in complex control and management systems. The approach is based on stochastic programming, the potential function method and on control theory. In the paper a control design for optimal control and stabilization of the specific growth rate of fed-batch biotechnological processes is presented. The control design is based on the Wang-Monod and Wang-Yerusalimsky kinetic models and their equivalent Brunovsky normal form. The control is written based on information of the growth rate. The mathematical formulations, presented here serve as basis for the tool development. The evaluation leads to the development of preferences-based decision support in machine learning environments and iterative complex control descriptions and design.
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