A fuzzy controller-based multiple-model adaptive control system for blood pressure control

This paper presents a fuzzy controller-based multiple-model adaptive control system for blood pressure control. We use multiple-model adaptive control (MMAC) algorithm to identify the patient model, as the transfer function parameters are different for each patient and often change with time; and we use fuzzy control (FC) method 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 is adjusted continuously according to error and rate change of error of the process output. For each fuzzy controller, the gains become larger when process output is far from desired set-point, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved.