NDLI: Automated postoperative blood pressure control

Content Provider	Springer Nature Link
Author	Zheng, Hang Zhu, Kuanyi
Copyright Year	2005
Abstract	It is very important to maintain the level of mean arterial pressure (MAP) . The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient’s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control (MMAC) algorithm is used to identify the patient model, and it is a feasible system identification. method even in the presence of large noise. Fuzzy control (FC) method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral (PI) controller, whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient’s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.
Starting Page	207
Ending Page	212
Page Count	6
File Format	PDF
ISSN	16726340
Journal	Journal of Control Theory and Applications
Volume Number	3
Issue Number	3
e-ISSN	19930623
Language	English
Publisher	Editorial Board of Control Theory & Applications
Publisher Date	2005-01-01
Publisher Place	Guangzhou
Access Restriction	Subscribed
Subject Keyword	Multiple-model adaptive control Fuzzy control Blood pressure control Cardiovascular modeling Control Structures and Microprogramming
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications Hardware and Architecture

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

State-shared model for multiple-input multiple-output systems

Intelligent control using multiple models based on on-line learning

Improved adaptive fuzzy control for MIMO nonlinear time-delay systems

Adaptive switching control of discrete time nonlinear systems based on multiple models

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults

Three-dimensional fuzzy logic system for process modeling and control

Automated postoperative blood pressure control

Similar Documents

Robust adaptive fuzzy control scheme for nonlinear system with uncertainty

Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

State-shared model for multiple-input multiple-output systems

Intelligent control using multiple models based on on-line learning

Improved adaptive fuzzy control for MIMO nonlinear time-delay systems

Adaptive switching control of discrete time nonlinear systems based on multiple models

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults

Three-dimensional fuzzy logic system for process modeling and control

Automated postoperative blood pressure control