A dual-rate Kalman filter is developed for realtime continuous glucose monitoring. Frequent (5 minute) sampling of a noisy, continuous glucose sensor is used for estimation of glucose and its rate-of-change. Infrequent (8 hour intervals) reference glucose meter samples enable the sensor gain and its rate-of-change to be updated. The dual-rate Kalman filter formulation accounts for uncertainty in both the continuous glucose sensor and the reference glucose meter. The method is tested on simulated and experimental data, confirming its superiority to simple one-point calibration.
Classical model-based control strategies assume a single disturbance model. In practice, the type of disturbance is often unknown or can change with time or multiple different disturbance types can occur simultaneously. In this paper, a multiple model predictive control strategy is developed to handle different disturbances, including multiple disturbances occurring simultaneously. A detailed discussion of disturbance model bank generation, state estimation, and disturbance model weighting is provided, and an unconstrained multiple model predictive control solution is formulated. Simulation results demonstrate successful estimation and control of single and multiple simultaneous disturbances.
An augmented state formulation for multiple model predictive control (MMPC) is developed to improve the regulation of nonlinear and uncertain process systems. By augmenting disturbances as states that are estimated using a Kalman filter, improved disturbance rejection is achieved compared to an additive output disturbance assumption. The approach is applied to a quadratic tank example, which has challenging dynamic behavior, switching from minimum phase to nonminimum phase behavior as the operating conditions are changed.
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