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In the pharmaceutical industry, it is common to determine the maximum allowable set of uncertain parameters—called the design space—that guarantees that the product quality attributes obtained by a manufacturing process are within specified limits. In industrial practice, design spaces are constructed without considering the effects of process dynamics or feedback control, which results in poor characterization...
In the Quality-by-Design (QbD) paradigm for pharmaceutical processes, critical quality attributes (CQAs) must meet specifications for all possible realizations of critical process parameters (CPPs) within a design space. During the startup of such a process, it is desired to begin meeting CQA specifications as quickly as possible and then robustly guarantee that specifications will continue to be...
A commonly applied procedure in the pharmaceutical industry is to determine the maximum allowable set of uncertain parameters—called the design space—that guarantees that the product quality attributes obtained by a manufacturing process are within the specific limits. Design spaces in industry are constructed without considering the effects of process dynamics or feedback control, which results in...
This paper reviews the use of linear matrix inequalities (LMIs) and bilinear matrix inequalities (BMIs) in the formulation of many important problems in process control such as: control structure selection, robust controller analysis and design, parameter estimation, and optimal design of experiments.
Feedback control problems for distributed parameter systems arise in a variety of physical, chemical, biological, and mechanical systems. This paper exploits the algebraic structure of the system of ordinary differential equations that arise from spatial discretization of the partial differential equation (PDE) to analyze and design feedback controllers that are robust to bounded perturbations in...
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