Proceedings of the ... American Control Conference. American Control Conference最新文献

The controllability of a dynamical system or network describes whether a given set of control inputs can completely exert influence in order to drive the system towards a desired state. Structural controllability develops the canonical coupling structures in a network that lead to un-controllability, but does not account for the effects of explicit symmetries contained in a network. Recent work has made use of this framework to determine the minimum number and location of the optimal actuators necessary to completely control complex networks. In systems or networks with structural symmetries, group representation theory provides the mechanisms for how the symmetry contained in a network will influence its controllability, and thus affects the placement of these critical actuators, which is a topic of broad interest in science from ecological, biological and man-made networks to engineering systems and design.

The sample frequency and volume of blood that can be drawn from a single patient is meticulously restricted under the human subject protection protocols established by an institutional review board (IRB). Consequently, the amount of samples that can be taken during a particular experiment is limited. In order to ensure an effective experiment design, considerations must be taken choosing when to take patient samples. A validated model of HIV-1 viral replication and 2-LTR production is exploited to find sub-optimal sampling schedules that maximize information content of the experiment outcome. This is done through a Forward Stepwise Regression (FSR) process with Kullback Liebler Divergence (KLD) as a selection criterion. Suboptimal schedules are found for an experiment taking four sample points over a possible span of 20 weeks. All schedules found with the FSR process contain significantly more information than both a uniform schedule and a schedule used in a previous experiment with 4 sample points. This work demonstrates the advantages of using KLD as a tool in the experiment design process to increase information content.

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes mellitus is considered. The contribution of this paper is to propose a velocity-weighting mechanism, within an MPC problem's cost function, that facilitates penalizing predicted hyperglycemic blood-glucose excursions based on the predicted blood-glucose levels' rates of change. The method provides the control designer some freedom for independently shaping the AP's uphill versus downhill responses to hyperglycemic excursions; of particular emphasis in this paper is the downhill response. The proposal aims to tackle the dangerous issue of controller-induced hypoglycemia following large hyperglycemic excursions, e.g., after meals, that results in part due to the large delays of subcutaneous glucose sensing and subcutaneous insulin infusion - the case considered here. The efficacy of the proposed approach is demonstrated using the University of Virginia/Padova metabolic simulator with both unannounced and announced meal scenarios.

The design of a Model Predictive Control (MPC) strategy for the closed-loop operation of an Artificial Pancreas (AP) for treating Type 1 Diabetes Mellitus (T1DM) is considered in this paper. The contribution of this paper is to propose two changes to the usual structure of the MPC problems typically considered for control of an AP. The first proposed change is to replace the symmetric, quadratic input cost function with an asymmetric, quadratic function, allowing negative control inputs to be penalized less than positive ones. This facilitates rapid pump-suspensions in response to predicted hypoglycemia, while simultaneously permitting the design of a conservative response to hyperglycemia. The second proposed change is to penalize the velocity of the predicted glucose level, where this velocity penalty is based on a cost function that is again asymmetric, but additionally state-dependent. This facilitates the accelerated response to acute, persistent hyperglycemic events, e.g., as induced by unannounced meals. The novel functionality is demonstrated by numerical examples, and the efficacy of the proposed MPC strategy verified using the University of Padova/Virginia metabolic simulator.