Abstract: A real-time glucose estimator uses a linearized Kalman filter to determine a best estimate of glucose level in real time. The real-time glucose estimator receives at least one measurement corresponding to glucose level. The measurement can be obtained with one or more sensors and is provided to the linearized Kalman filter in real time. The linearized Kalman filter has dynamic models and executes a recursive routine to determine the best estimate of glucose level based upon the measurement. Additional information can be provided to the linearized Kalman filter for initialization, configuration, and the like. Outputs of the linearized Kalman filter can be provided to a patient health monitor for display or for statistical testing to determine status of the real-time glucose estimator. The real-time glucose estimator can be implemented using a software algorithm.

Abstract: A real-time controller operating as an artificial pancreas uses a Kalman control algorithm to control glucose level of a patient in real time. The real-time controller receives an estimate of the patient glucose level and a reference glucose level. The estimate of the patient glucose level can be provided by an optimal estimator implemented using a linearized Kalman filter. The estimated glucose level and the reference glucose level are processed by the Kalman control algorithm to determine a control command in real time. The Kalman control algorithm has a dynamic process forced by the control command a cost function determining a relative level of control. The control command is provided to a dispenser which secretes insulin or glucagon in response to the control command to correct a relatively high glucose level or a relatively low glucose level.