Nonlinear model predictive control with moving horizon state and disturbance estimation - Application to the normalization of blood glucose in the critically ill

Abstract In this paper we present a nonlinear model predictive control (NMPC) strategy that can be used to tackle nonlinear control problems with changing model parameters, unknown disturbance factors and specifications on the rates of change of the inputs. The closed-loop performance of the proposed NMPC strategy is demonstrated by applying it to the problem of blood glucose normalization in critically ill patients. A nonlinear patient model, that is particularly developed for describing the glucose and the insulin dynamics of these patients, is used for online state and disturbance estimation and control under a realistic disturbance realization. The results are satisfactory both in terms of control behavior (set point tracking and the suppression of unknown disturbance factors) and clinical acceptability.

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