B. Aufderheide

Extension of dynamic matrix control to multiple models

The purpose of the paper is to extend dynamic matrix control (DMC) to handle different operating regimes and to reject parameter disturbances. This is done by two new multiple model predictive control (MMPC) schemes: one based on actual step response tests and the other on a minimal knowledge based first order plus dead time models (FOPDT). Both approaches do not require fundamental modeling. As a benchmark comparison, the two controllers are compared with a nonlinear model predictive controller (NL-MPC) using an extended Kalman filter (EKF) with no initial model/plant mismatch. The application example is the isothermal Van de Vusse reaction, which exhibits challenging input multiplicity. Simulations include disturbances in the feed concentration, kinetic parameters, and additive input and output noise. The two controllers have comparable performance to NL-MPC and in the case of multiple disturbances can outperform NL-MPC.

Design and implementation of three incoherent feed-forward motif based biological concentration sensors.

Synthetic biology is a useful tool to investigate the dynamics of small biological networks and to assess our capacity to predict their behavior from computational models. In this work we report the construction of three different synthetic networks in Escherichia coli based upon the incoherent feed-forward loop architecture. The steady state behavior of the networks was investigated experimentally and computationally under different mutational regimes in a population based assay. Our data shows that the three incoherent feed-forward networks, using three different macromolecular inhibitory elements, reproduce the behavior predicted from our computational model. We also demonstrate that specific biological motifs can be designed to generate similar behavior using different components. In addition we show how it is possible to tune the behavior of the networks in a predicable manner by applying suitable mutations to the inhibitory elements.

Automated regulation of hemodynamic variables

Experimental studies of two control methodologies for regulating multiple variables in critical care patients are described. The control strategies for the regulation of mean arterial pressure and cardiac output use vasoactive and inotropic drugs. Corresponding experimental results from the evaluation of the controllers with canines are presented.

Multiple model predictive control of hemodynamic variables: an experimental study

A multiple model predictive controller is designed to regulate mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The algorithm uses a multiple model adaptive approach in a model predictive control framework to account for inter- and intra-patient variability and explicitly handle drug rate constraints. The controller is experimentally evaluated on canines that are pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.

A comparison of fundamental model-based and multiple model predictive control

A multiple model strategy is implemented in a model predictive control framework. The model bank design requires minimal plant knowledge based on the ranges of gains, dominant time constants and time delays. The application example is the isothermal Van de Vusse reaction in a continuous stirred tank reactor, which exhibits challenging input multiplicity behavior. Disturbances include additive input and output noises and changes in system parameters. Results are compared with an extended Kalman filter (EKF)-based model predictive controller that uses a fundamental model with a disturbance parameter estimated online. The multiple model predictive controller performance is comparable to that demonstrated by the EKF-based model predictive controller.

Multiple model predictive control of canine hemodynamic variables

A multiple model predictive controller is designed to regulate mean arterial pressure, and cardiac output using inotropic and vasoactive drugs. The algorithm accounts for inter- and intra-patient variability and explicitly handles drug rate constraints. It is experimentally evaluated on canines that are pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.

Automated hemodynamic regulation with model predictive control

A multiple model adaptive predictive controller is designed to regulate mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The algorithm uses a multiple model adaptive approach in a model predictive control framework to account for inter- and intra-patient variability and explicitly handle drug rate constraints. The controller is evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.