臺灣博碩士論文加值系統

對於在醫院常需要使用降血壓藥物, 如 Sodium Nitroprusside (SNP) 來降低動脈平
均壓並維持在某一特定血壓值的病人, 自動迴路控制系統具有相當的應用價值. 由護 士
或醫師人為調整 SNP 流量將是極為繁瑣且耗時的工作, 同時常常無法及時調整或過量而
導至病人不必要甚致危險性的平均壓振動或過低量. 自動控制動脈血壓系統包含一血壓放
大器或床邊監視器, 其經由導管端之壓電換能器來量測血壓波形, 計算出平均壓值並作為
回饋控制信號; 以個人電腦 (PC) 作為控制器, 依其演算法則計算並輸出藥物輸液流速值
; 以輸液幫浦依流速指令將所需藥量靜脈注射予病人, 達成自動控制血壓之臨床目標.
基於動脈平鈞壓對 SNP 流速反應之數學模型為延遲 (time-delay) 時變 (time-varying
)非線性 (non-linear) 且富含雜訊之單輸入/輸出 (SISO) 系統 ; 同時, 不同病人間之
系統增益值 (gain) 變動範圍極大 (36:1), 治療期間之各系統參數值亦會隨生理狀況而
隨時變動, 因此, 設計一不需預先知道或估測受控系統之參數值, 並能滿足特定控制性能
與臨床限制需求, 且為強健 (robust) 控制之動脈血壓控制器, 一直為 1980 年代以來所
關切之課題. 本論文設計一混合式智慧型控制器以閉迴路適應控制動脈血壓, 採用平
行雙模組多層類神經網路 (parallel two-model multilayer neural network, MNN) 架
構, 並結合模糊邏輯單元 (fuzzy-logic unit) 或法則庫單元 (rule-based unit) 之新
型控制器,其中一 MNN 模組映射 (map) 學習增益值較大範圍之受控系統特性, 另一模組
則映射學習增益值較小範圍之受控系統特性, 使用修正之後饋傳遞學習法則 (modified b
ack propagation learning algorithm) 以整合地快速趨近受控系統之大範圍參數值與時
變特性; 模糊邏輯單元或法則庫單元依平均壓誤差訊號值與誤差訊號之時間變率值, 計算
並決定雙模組 MNN 輸出權值因子 (output weighting factor) 之各別起始值與增值,
並各別依次更新輸出權值因子, 再總算以產生適當的控制信號, 用來改變輸液幫浦之輸液
流速值. 研究結果顯示, 此混合式智慧型控制器具備以下特點: 1) 不需預先知道或估
測受控系統模型之參數值, 即可直接線上 (on-line) 應用, 並具有良好強健性; 2) 面對
絕大 部分之全範圍受控系統參數值, 參數值變動及不確定狀況, 大幅度雜訊值干擾, 皆
能得 到滿意的動態特性, 穩態極小誤差, 以及符合臨床限制需求之性能; 3) 良好的血壓
控制指令追蹤 (tracking) 能力; 4) 良好的時間延遲不確定與變動之忍受能力, 即不需
預先知道或估測受控系統時間延遲之最大階數 (order), 亦不會導致系統不穩定或明顯降
低控制性能; 5) 設計架構與演算法則較為簡單, 易於實施.

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.

COVER
TABLE OF CONTENTS
LIST OF FIGURES
LIST OF TABLES
ABSTRACT
CHAPTER 1 : INTRODUCTION
1.1 Background
1.2 Overview of controller for application of blood presure control
1.3 Definition of problem investigated
1.4 Research purpose
1.5 Outline of the dissertation
CHAPTER 2 : PLANT CHARACTERISTICS
2.1 Blood pressure response to a bolus infusion of SNP drug
2.2 Mathematical model of plant
CHAPTER 3 : DESIGN OF HYBRID INTELLIGENT CONTROLLER
3.1 Performance requirements of control system
3.2 Clinical requirements of input/output constraints
3.3 Controller structure
3.4 Design of two-model multilayer neural network (MNN) controller
3.5 Design of weighting-determinant unit (WDU)
CHAPTER 4 : RESULTS and DISCUSSION
4.1 Results
4.2 Discussion
CHAPTER 5 : CONCLUSION and FUTURE WORK
5.1 Conclusion
5.2 Future work
REFERENCES