Misconception of Glycemic Control Algorithms B. Wayne Bequette, Rensselaer Polytechnic Institute 3 January 2012 Hoekstra and coworkers  review the technologies available for computerized glucose regulation in the intensive care unit, but misrepresent the differences between two control algorithms, Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC). The differences between PID and MPC are illustrated by an example of an automobile on a roadway. They claim that the driver using MPC determines his/her driving strategy before departing, and maintains that trajectory throughout the trip. They also claim that the driver using PID makes frequent control action changes based on the difference between the ¿ideal¿ and actual trajectory. The MPC scenario shown is largely incorrect. MPC looks into the future (down the roadway) and determines the best sequence of control actions (driving strategy) to maintain that future trajectory. MPC does not simply implement that entire sequence of control actions (steering, braking, etc.), but, instead, updates the control actions at frequent intervals, in the same way that PID makes adjustments at frequent time intervals. Thus, the MPC strategy is no more sensitive to ¿small errors in input variables¿ than the PID strategy. It has been a common misconception that PID is less sensitive than MPC to uncertainty in the system dynamics. In reality, MPC is no more sensitive to uncertainty than PID, if the two strategies are tuned for the same performance. Undergraduate textbooks  show that internal model control (IMC), a model-based control strategy, can be implemented as an equivalent PID controller when low-order models are used as the basis for controller design; this is also shown by Percival and co-workers . The ¿take home message¿ is, if PID and MPC algorithms are tuned for the same level of performance, they have exactly the same sensitivity to uncertainty. References 1. Hoekstra M, Vogelzang M, Verbitsky E, Nijsten MWN: Health technology assessment review: Computerized glucose regulation in the intensive care unit ¿ how to create artificial control. Critical Care 2009, 13:223 (doi:10.1186/cc8023) 2. Bequette BW: Process Control: Modeling, Design and Simulation. New Jersey: Prentice Hall; 2003. 3. Percival MW, Zisser H, Jovanovic L, Doyle III FJ: Closed-loop control and advisory mode evaluation of an artificial pancreatic b cell: Use of proportional-integral-derivative equivalent model-based controllers. J. Diabetes Sci Technol 2008, 2:636-644. Competing interests None.