Robert Parker

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  Modeling Practices for Control - a Model Predictive Control (MPC) Tutorial
  Robert Parker

  The concept of automated closed-loop feedback control has been used in the chemical industries since the 1950s. While these feedback control methods work well in industrial practice, the challenges of biological systems -- including comparative data sparsity, a lack of full state measurements, challenging dynamics, and nonlinearity -- often lead to undesirable performance using simple feedback controllers. This talk will introduce Model Predictive Control, which serves as the algorithm driving many of the closed-loop insulin pumps in development for insulin-dependent diabetic patients. By developing an accurate model of they physical system, and using that model explicitly in the synthesis and solution of the on-line control problem, performance superior to that offered by feedback control is possible, even for challenging biological systems. This talk will provide an overview of model construction from first-principles knowledge as well as data-driven modeling, as well as the synthesis of the MPC controller to achieve clinically-relevant performance objectives. An example application of these tools will be discussed in the context of glucose control for diabetic and/or critical care patients.