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

The recent introduction of integrase inhibitors to the HIV antiviral repertoire permits us to create in vitro experiments that reliably terminate HIV infection at the point of chromosomal integration. This allows us to isolate the dynamics of a single round of infection, without needing to account for the influence of multiple overlapping rounds of infection. By measuring the various nucleic acid concentrations in a population of infected target cells at multiple time points, we can infer the rates of these molecular events with great accuracy, which allows us to compare the rates between target cells with different functional phenotypes. This information will help in understanding the behavior of the various populations of reservoir cells such as active and quiescent T-cells which maintain HIV infection in treated patients. In this paper, we introduce a family of models of the early molecular events in HIV infection, with either linear dynamics or age-structured delays at each step. We introduce an experimental design metric based on the delta AIC (Akaike Information Criteria) between a model fit for simulated data from a matching model vs a mismatched model, which allows us to determine a candidate experiment design's ability to discriminate between models. Using parameters values drawn from experimentally-derived priors corrupted with appropriate measurement noise, we confirm that a proposed sampling schedule at different time points allows us to consistently discriminate between candidate models.

Current robotic exoskeletons enforce fixed reference joint patterns during gait rehabilitation. These control methods aim to replicate normative joint kinematics but do not facilitate learning patient-specific kinematics. Trajectory-free control methods for exoskeletons are required to promote user control over joint kinematics. Our prior work on potential energy shaping provides virtual body-weight support through a trajectory-free control law, but altering only the gravitational forces does not assist the subject in accelerating/decelerating the body forward. Kinetic energy is velocity dependent and thus shaping the kinetic energy in addition to potential energy can yield greater dynamical changes in closed loop. In this paper, we generalize our previous work to achieve underactuated total energy shaping of the human body through a lower-limb exoskeleton. By shaping the fully-actuated part of the body's mass matrix, we satisfy the matching condition for different contact phases and obtain trajectory-free control laws. Simulations of a human-like biped demonstrate speed regulation in addition to body-weight support, indicating the potential clinical value of this control approach.

Excessive gestational weight gain (i.e., weight gain during pregnancy) is a significant public health concern, and has been the recent focus of novel, control systems-based interventions. This paper develops a control-oriented dynamical systems model based on a first-principles energy balance model from the literature, which is evaluated against participant data from a study targeted to obese and overweight pregnant women. The results indicate significant under-reporting of energy intake among the participant population. A series of approaches based on system identification and state estimation are developed in the paper to better understand and characterize the extent of under-reporting; these range from back-calculating energy intake from a closed-form of the energy balance model, to a constrained semi-physical identification approach that estimates the extent of systematic under-reporting in the presence of noise and possibly missing data. Additionally, we describe an adaptive algorithm based on Kalman filtering to estimate energy intake in real-time. The approaches are illustrated with data from both simulated and actual intervention participants.

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.