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In this study, a two-level scheme with robust dynamic real-time optimization and distributed model predictive control (DMPC) is presented for plant-wide processes. On the top layer, chance-constrained programming (CCP) is employed in the real-time optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level...
This paper deals with the closed-loop identification of a two-tank process used in industry. The identified model is then utilized to develop robust controllers i.e. H E; and sliding mode controllers. It is shown that these controllers guarantee a satisfactory performance in the face of both model/parametric uncertainties and external disturbances. The designed controllers have been successfully tested...
In this paper the control of a bioprocess using an adaptive type-2 fuzzy logic controller is proposed. The process is concerned with the aerobic alcoholic fermentation for the growth of Saccharomyces Cerevisiae and is characterized by nonlinearity and parameter uncertainty. Three type-2 fuzzy controllers heve been developed and tested by simulation: a simple type-2 fuzzy logic controller with 49 rules;...
A novel integration of design and controllability method is proposed for the process design under uncertainty. The key idea is to integrate the robust eigenvalue assignment with the robust optimal design to achieve the desirable design performance and dynamic performance. The robust optimal design is to have a trade-off between robustness and optimization for the steady-state economic design, and...
This paper deals with the identification and robust control of a biological wastewater treatment process. The identification has been done on the basis of the minimization of an Euclidian-distance criterion, with respect to the model coefficients. The robust control of the biological wastewater treatment process has been accomplished through QFT (quantitative feedback theory) techniques. To this end...
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