Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

Bharat Singh (Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India) and Shabana Urooj (Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJNCR.2018070103


Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).
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The consistent improvement of new medication systems is focused by augmented therapeutic activity while limiting negative reactions (Rösler et al., 2012). The patient’s operating point is described by controlling MABP of a pre-surgical patient. MABP can be controlled by Sodium Nitroprusside (SNP), which is a drug that lowers the blood pressure by reducing the strain in arterioles walls (Malagutti et al., 2013).

The infusion rate of SNP can be regulated by a conventional control system; a closed-loop control can be characterized by the administration of single- or multiple-output values of a system following a specific target value. Its goal is to compute solutions for an exact remedial output of controller will produce system stability (Neckebroek et al., 2013). A closed loop proportional–integral–derivative controller (PID) control has been widely applied in many real applications. Also, some other well-known methods are Ziegler-Nichols method, IMC method, and loop-shaping method (Jianda et al., 2016). The average value of error will be detected by PID controller and it will minimize the error. PID controller are widely used in drug delivery system since it is trustworthy also tuning methods are used to improve the efficiency of PID controller (Shabani et al., 2012).

The appropriate clinical control performance cannot be accomplished with a single controller furthermore this has been recognized in previous researches, leading to a versatile adaptive control strategies being recommended throughout previous three decades that includes multiple-model adaptive control (MMAC), model-reference adaptive control and model-predictive control, self-tuning regulators, and in addition fuzzy control and rule-based non-linear control (Abbasi et al., 2015; Huang et al., 2012; Méndez et al., 2013). Among these methods fuzzy base PID controller can be used in drug delivery system for guaranteed stability and accuracy.

In recent control application fuzzy logic controllers (FLC) have turned out to be more popular to handle complex nonlinear systems. It has been appeared in numerous present-day research that FLC implementation helps online tuning of controller parameter in terms of finding operating point for nonlinear system improves the closed loop performance of PID controller. But the implementation of PID tuning reduces the property of fuzzy PID controller, it may cause distorted control output (Sheng &Bao, 2013) (Duan et al., 2013). To overcome this problem a Fractional Order Fuzzy Proportional-Integral-Derivative (FOFPID) can be implemented with a digital filter, which is used for stabilizing blood pressure fluctuation. Fractional order modeling and fractional order controllers have received latest acceptance in the control engineering because of its additional adaptability to get it, characterize control dynamical systems (Sharma et al., 2014; Das et al., 2013; Liu et al., 2014).

Fractional-order systems, is the fundamental logic for FOFPID, even non-integer-order systems which is actually extension of integer order system can easily model the nonlinear system without changing the inherent property of FLC (Mishra et al., 2015). FOFPID are also used in speed regulation of machines such as turbines. According to Takagi-Sugeno (TS), it is clear that the implementation of non-integer order of differentiation and integration operator helped the system more robust and made the controller model based adaptive (Tarasov et al., 2014).

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