Automated postoperative blood pressure control

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.

Automated postoperative blood pressure control

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. Hang ZHENG graduated from the University of Science and Technology of China, in 2001. Currently, he is a master student in School of Electrical and Electronic Engineering of Nanyang Technological University, Singapore. His research interests include nonlinear time-varying system control, multiple model adaptive control and fuzzy control. Email: zhenOOO8@ntu.edu.sg. Kuanyi ZHU graduated from Northeastern University, China, in 1982. He received his M.E. and Ph. D from University of Louvain-La-Neuve, in Belgium in 1986 and 1989, respectively. Currently, he is associate professor with School of Electrical and Electronic Engineering of Nanyang Technological University, Singapore. His research interests include model-based predictive control, biomedical system control and process control. Email:ekyzhu@ntu.edu.sg.