Abstract
Controlled drug delivery system (DDS) is an electromechanical device that enables the injection of a therapeutic drug intravenously in the human body and improves its effectiveness and care by controlling the rate and time of drug release. Controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operation. Different methods have been proposed for controlling MABP; all methods have certain disadvantages according to patient model. In this paper, we have proposed blood pressure control using integral reinforcement learning-based fuzzy inference system (IRLFI) based on parameter estimation technique. To further increase the safety of the proposed method, a supervisory algorithm is implemented, which maintains the infusion rate within safety limit. MATLAB simulation depends the model of MABP, elucidate the ability of the suggested methodology in designing DDS and control postsurgical MABP.
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Singh, B., Urooj, S. (2018). Adaptive Parameter Estimation-Based Drug Delivery System for Blood Pressure Regulation. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_48
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DOI: https://doi.org/10.1007/978-981-10-7563-6_48
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