An Efficient Multiple Models Based Predictive Control Approach with its Application to Mean Arterial Blood Pressure Process

Nonlinear behavior and disturbance sensitivity of the blood pressure processes cause them to be known as an appropriate test bench for advanced control approaches. Because of special behavior and varying parameters of blood pressure processes, multiple models predictive control approaches (MMPC) have the better performance than other ones from both regulation and disturbance rejection points of views. Supervisory method based on prediction error for the MMPC is illustrated. Better regulation in special condition and most excellent disturbance rejection are achieved.