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.

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.

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.

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 strapped down astro-inertial navigator includes a roll outer gimbal, a pitch inner gimbal and a platform coupled to the inner gimbal. An instrument cluster which includes X-axis, Y-axis and Z-axis ring laser gyros (RLGs) and associated accelerometers is mounted to the platform. Also hard mounted to the platform is a stellar sensor which includes a telescope and a solid state focal plane array which views stellar reference objects. In an astro-inertial mode of operation one or more stellar objects are tracked for a period of time. The roll and the pitch gimbals are employed to point the telescope and, periodically, to observe and average out the effects of star sensor and horizontal accelerometer errors. The star sensor error observability is accomplished by periodically rotating the inner and outer gimbals through 180.degree. and the stellar objects once again tracked. Due to the 180.degree. rotation boresight errors within the stellar tracker are observed and compensated for. The 180.degree.

Abstract: A navigation system 12 for an evasive maneuvering reentry vehicle (EMRV) 10 includes a three-axes strapdown inertial navigator 14 which includes RLGs or other types of gyroscopes and also accelerometers. An altimeter, such as a radar altimeter 16, which is normally provided on the EMRV 10, provides an output (h) to the navigation system 12, the output being expressive of the altitude of the EMRV 10 from the earth's surface. The altitude measurement output (h) is processed by a statistical filter, such as a Kalman filter 18, in conjunction with the inertial solution (X) from the navigator 14. The output of the filter 18 is an estimated inertial correction which is combined with the inertial position and velocity output of the navigator 14 at a block 20 to yield a corrected position and velocity output. It is shown that a single altitude measurement is sufficient to derive three position and three velocity compensations for the EMRV 10.