An adaptive blood pressure control system based on the long-range or finite-time-horizon prediction strategy known as generalized predictive control has been examined. The system was designed to run as a real-time, multitasking operating system providing real-time monitoring and control of mean arterial pressure. The ability to control in the presence of constraints was also incorporated into the system.Results from both unconstrained and constrained control runs were obtained from experiments on aneasthetized dogs. The problems caused by model-plant mismatch, varying time delays and nonlinearity were handled satisfactorily by the system. The disturbance rejection of the constrained algorithm is better than the unconstrained one. The results demonstrate the applicability of both the constrained and unconstrained generalized predictive control algorithm for regulation of mean arterial pressure.
Post-operative drug therapy often requires blood-pressure regulation in many patients. The commonly used vasodilator, sodium nitroprusside, is routinely used in clinical practice f o r induced hypotension. Because of the drug's fast-acting nature and potency, a lot of attention has been given t o the development of automated drug infusion systems, with which a patient's blood pressure can be monitored and regulated automatically. Some closed-loop control systems based on Proportional and Integral feedback have been reported t o have performed satisfactorily in clinical practice. However, improvement in robustness is required, i.e. consistent performance of the automatic infusion system when used on different patients is required without retuning the controller.J J E A Ay(t+j) = E B Au(t+j-l) + E C <(t+j)(3) J J J By substituting eqn.(2) into eqn. (31, the incremental blood pressure signal becomes a function of the past drug infusion rates, current and past blood pressure measurements, a s well a s a future noise term, Ay(t+j) = F y (t) + E B Auf(t+j-l) + E <(t+j)J f J J
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