A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems: Application to Optimized Interventions for Physical Activity.

Hybrid Model Predictive Control (HMPC) is presented as a decision-making tool for novel behavioral interventions to increase physical activity in sedentary adults, such as . A broad-based HMPC formulation for mixed logical dynamical (MLD) systems relevant to problems in behavioral medicine is developed and illustrated on a representative participant model arising from the study. The MLD model is developed based on the requirement of granting points for meeting daily step goals and categorical input variables. The algorithm features three degrees-of-freedom tuning for setpoint tracking, measured and unmeasured disturbance rejection that facilitates controller robustness; disturbance anticipation further improves performance for upcoming events such as weekends and weather forecasts. To avoid the corresponding mixed-integer quadratic problem (MIQP) from becoming infeasible, slack variables are introduced in the objective function. Simulation results indicate that the proposed HMPC scheme effectively manages hybrid dynamics, setpoint tracking, disturbance rejection, and the transition between the two phases of the intervention (initiation and maintenance) and is suitable for evaluation in clinical trials.