{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/43059"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/43059","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"An analytical and experimental investigation of respiratory dynamics using P/D control and carbon dioxide feedback","abstract":"This thesis addresses the problem of defining the control law for human respiration. Seven different drivers have been identified as possibly having an input to the respiratory controller. These seven represent a combination of feedforward and feedback inputs arising from neural and humoral mechanisms. Using the assumption that carbon dioxide concentrations in the arterial blood have the strongest effect, a control equation with proportional and derivative components based on this driver was evaluated. The methodology for the evaluation was to create a model of the respiratory system incorporating the P/D controller, obtain experimental data of one test subject's respiratory response to exercise, then compare model generated output with experimental data, and adjust the parameters in the control equation to yield optimal model performance. The usual practice of testing controller performance has been to apply single step loads to a model and evaluate its response. A multi-step protocol was used here to provide a better, more generalized test of controller performance. This thesis may represent the first documented use of an approach of this type for evaluating respiratory controller performance. Application of a multi-step protocol revealed a non-linear controller was needed to keep pace with system changes. Respiratory system operation was effectively managed using a controller of the form: VENTILATION = F(dCO₂/dT,Q) + F(CO₂,Q) + CONSTANT.","abstract_html":"This thesis addresses the problem of defining the control law for human respiration. Seven different drivers have been identified as possibly having an input to the respiratory controller. These seven represent a combination of feedforward and feedback inputs arising from neural and humoral mechanisms. Using the assumption that carbon dioxide concentrations in the arterial blood have the strongest effect, a control equation with proportional and derivative components based on this driver was evaluated. The methodology for the evaluation was to create a model of the respiratory system incorporating the P/D controller, obtain experimental data of one test subject's respiratory response to exercise, then compare model generated output with experimental data, and adjust the parameters in the control equation to yield optimal model performance. The usual practice of testing controller performance has been to apply single step loads to a model and evaluate its response. A multi-step protocol was used here to provide a better, more generalized test of controller performance. This thesis may represent the first documented use of an approach of this type for evaluating respiratory controller performance. Application of a multi-step protocol revealed a non-linear controller was needed to keep pace with system changes. 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