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

We consider the problem of controlling populations of interconnected neurons using extrinsic stimulation. Such a problem, which is relevant to applications in both basic neuroscience as well as brain medicine, is challenging due to the nonlinearity of neuronal dynamics and the highly unpredictable structure of underlying neuronal networks. Compounding this difficulty is the fact that most neurostimulation technologies offer a single degree of freedom to actuate tens to hundreds of interconnected neurons. To meet these challenges, here we consider an adaptive, learning-based approach to controlling neural spike trains. Rather than explicitly modeling neural dynamics and designing optimal controls, we instead synthesize a so-called control network (CONET) that interacts with the spiking network by maximizing the Shannon mutual information between it and the realized spiking outputs. Thus, the CONET learns a representation of the spiking network that subsequently allows it to learn suitable control signals through a reinforcement-type mechanism. We demonstrate feasibility of the approach by controlling networks of stochastic spiking neurons, wherein desired patterns are induced for neuron-to-actuator ratios in excess of 10 to 1.

This paper offers a novel generalization of a passivity-based, energy tracking controller for robust bipedal walking. Past work has shown that a biped limit cycle with a known, constant mechanical energy can be made robust to uneven terrains and disturbances by actively driving energy to that reference. However, the assumption of a known, constant mechanical energy has limited application of this passivity-based method to simple toy models (often passive walkers). The method presented in this paper allows the passivity-based controller to be used in combination with an arbitrary inner-loop control that creates a limit cycle with a constant generalized system energy. We also show that the proposed control method accommodates arbitrary degrees of underactuation. Simulations on a 7-link biped model demonstrate that the proposed control scheme enlarges the basin of attraction, increases the convergence rate to the limit cycle, and improves robustness to ground slopes.

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.