DESIGN OF HYBRID INTELLIGENT CONTROLLER FOR ADAPTIVE CONTROL OF ARTERIAL BLOOD PRESSURE

• Main Authors: CHEN, CHIN-TE, 陳金德
• Other Authors: TE-SON KUO
• Format: Others
• Language: en_US
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/37181313665981095725

博士 === 國立臺灣大學 === 電機工程學系研究所 === 86 === The fast-acting drug sodium nitroprusside (SNP) is often administered to l ower mean arterial blood pressure (MABP) in hospitalized patients. It is tedio us, time-consuming and may yield undesirable or even hazardous oscillations in the level of MABP due to lack of timely adjustment of infusion or over-correc tion for the manual adjustment of the SNP infusion rate. Thus, closed-loop fee dback controllers is necessary to maintain MABP near a desired level because o f disturbances that perturb blood pressure, the changing condition of patient and the wide rangeof response characteristics among patients. The automatic ar terial blood pressurecontrol system is composed with a pressure/voltage piezoe lectric transducer mounted on femoral or brachial cannula, a blood pressure po lygraph or patient monitor, a personal computer (PC) as controller, and a medi cation drug infusion pump. The mathematical model of MABP of a patient und er the influence of SNP infusionrate is a time-delay, time-varying, nonlinear single-input/single-output (SISO) system and corrupted with much noise. The ga in of patient characteristic can vary as much as 36 fold from one patient to t he next. Furthermore, a patient''s characteristic also change during the course of therapy. Thus, the traditional control theory, such as nonadaptive control ler, optimal controller, single-model adaptive controller or multi-model contr oller, is difficult to achieve good and robust performance, and meet the clini cal constraints. In this thesis, a new hybrid intelligent control strategy is proposed by combining neural network and fuzzy-logic algorithms to control the time-varying single-input/single-output (SISO) system. A model with an aut oregressive moving average, representing the dynamics of the system, and a mod ified back-propagation training algorithm are used to design the control syste m to meet specified objectives of design (settling time and undershoot/oversho ot) and clinical constraints. We present a parallel two-model multilayer neura l network (MNN) controller structure to approximate the large dynamic range of parameter gains and time-varying plant. One MNN controller is to map the lear ned range of large-gain, and the other is for the range of small-gain, functio n of the system characteristics. The two-model MNN controller is also associat ed with a weighting determinant (WDU), such as fuzzy-logic unit (FLU) or rule- based unit, to determine an incremental value and update the output weighting factor of the parallel two-model MNN controller for adequate control action. Extensive computer simulations indicate satisfactory performance and robustn ess of the proposed controller in the presence of much noise, over the full ra nge of plant parameters, uncertainties and large variation of parameters, and no requirement ofsystem parameters identification a poriori, and good signal t racking capacity.