1,174 research outputs found

    The Dynamics of Hybrid Metabolic-Genetic Oscillators

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    The synthetic construction of intracellular circuits is frequently hindered by a poor knowledge of appropriate kinetics and precise rate parameters. Here, we use generalized modeling (GM) to study the dynamical behavior of topological models of a family of hybrid metabolic-genetic circuits known as "metabolators." Under mild assumptions on the kinetics, we use GM to analytically prove that all explicit kinetic models which are topologically analogous to one such circuit, the "core metabolator," cannot undergo Hopf bifurcations. Then, we examine more detailed models of the metabolator. Inspired by the experimental observation of a Hopf bifurcation in a synthetically constructed circuit related to the core metabolator, we apply GM to identify the critical components of the synthetically constructed metabolator which must be reintroduced in order to recover the Hopf bifurcation. Next, we study the dynamics of a re-wired version of the core metabolator, dubbed the "reverse" metabolator, and show that it exhibits a substantially richer set of dynamical behaviors, including both local and global oscillations. Prompted by the observation of relaxation oscillations in the reverse metabolator, we study the role that a separation of genetic and metabolic time scales may play in its dynamics, and find that widely separated time scales promote stability in the circuit. Our results illustrate a generic pipeline for vetting the potential success of a potential circuit design, simply by studying the dynamics of the corresponding generalized model

    Spin wave dispersion softening in the ferromagnetic Kondo lattice model for manganites

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    Spin dynamics is calculated in the ferromagnetic (FM) state of the generalized Kondo lattice model taking into account strong on-site correlations between e_g electrons and antiferromagnetic (AFM) exchange among t_{2g} spins. Our study suggests that competing FM double-exchange and AFM super-exchange interaction lead to a rather nontrivial spin-wave spectrum. While spin excitations have a conventional Dq^2 spectrum in the long-wavelength limit, there is a strong deviation from the spin-wave spectrum of the isotropic Heisenberg model close to the zone boundary. The relevance of our results to the experimental data are discussed.Comment: 6 RevTex pages, 3 embedded PostScript figure

    A Single-Loop DC Motor Control System Design with a Desired Aperiodic Degree of Stability

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    The application of the original analytical approach for Pi-controller synthesis of a stable second-order plant is considered. This approach allows finding controller parameters without any intensive computing by using the direct expressions. The plant model is obtained on the basis of identification, which is based on the automated real-interpolation method. The results of natural experiments are given

    Ball on a beam: stabilization under saturated input control with large basin of attraction

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    This article is devoted to the stabilization of two underactuated planar systems, the well-known straight beam-and-ball system and an original circular beam-and-ball system. The feedback control for each system is designed, using the Jordan form of its model, linearized near the unstable equilibrium. The limits on the voltage, fed to the motor, are taken into account explicitly. The straight beam-and-ball system has one unstable mode in the motion near the equilibrium point. The proposed control law ensures that the basin of attraction coincides with the controllability domain. The circular beam-and-ball system has two unstable modes near the equilibrium point. Therefore, this device, never considered in the past, is much more difficult to control than the straight beam-and-ball system. The main contribution is to propose a simple new control law, which ensures by adjusting its gain parameters that the basin of attraction arbitrarily can approach the controllability domain for the linear case. For both nonlinear systems, simulation results are presented to illustrate the efficiency of the designed nonlinear control laws and to determine the basin of attraction

    A method for the reconstruction of unknown non-monotonic growth functions in the chemostat

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    We propose an adaptive control law that allows one to identify unstable steady states of the open-loop system in the single-species chemostat model without the knowledge of the growth function. We then show how one can use this control law to trace out (reconstruct) the whole graph of the growth function. The process of tracing out the graph can be performed either continuously or step-wise. We present and compare both approaches. Even in the case of two species in competition, which is not directly accessible with our approach due to lack of controllability, feedback control improves identifiability of the non-dominant growth rate.Comment: expansion of ideas from proceedings paper (17 pages, 8 figures), proceedings paper is version v

    Active Learning in Persistent Surveillance UAV Missions

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    The performance of many complex UAV decision-making problems can be extremely sensitive to small errors in the model parameters. One way of mitigating this sensitivity is by designing algorithms that more effectively learn the model throughout the course of a mission. This paper addresses this important problem by considering model uncertainty in a multi-agent Markov Decision Process (MDP) and using an active learning approach to quickly learn transition model parameters. We build on previous research that allowed UAVs to passively update model parameter estimates by incorporating new state transition observations. In this work, however, the UAVs choose to actively reduce the uncertainty in their model parameters by taking exploratory and informative actions. These actions result in a faster adaptation and, by explicitly accounting for UAV fuel dynamics, also mitigates the risk of the exploration. This paper compares the nominal, passive learning approach against two methods for incorporating active learning into the MDP framework: (1) All state transitions are rewarded equally, and (2) State transition rewards are weighted according to the expected resulting reduction in the variance of the model parameter. In both cases, agent behaviors emerge that enable faster convergence of the uncertain model parameters to their true values

    Series-Parallel and Parallel Identification Schemes for a Class of Continuous Nonlinear Systems

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    Fig. 4(a) shows the parameter estimates under the existence of the measmable disturbance (di = 5, a\ = 0) with the parameter estimates under the ideal condition (di = a\ = 0) overlaid. Since the inserted DDR's remove the disturbance from the inputoutput relation, the disturbance does not slow down the identification speed. Fig. 4(6) shows the parameter estimates under the existence of the unmeasurable disturbance (di = 0, d 2 = 1) with the parameter estimates under the ideal condition (di = di = 0) overlaid. There exists no difference between the two cases as far as the identification speed is concerned. In the simulation, the step disturbances, di and d 2 , were injected to the plant at k = 0. Thus, strictly speaking, at k = 0, di(k) and d 2 (fc) did not satisfy equation V Conclusions Adverse effects of deterministic disturbances in linear identification have been pointed out, and a method to remove such effects has been presented. This method works for measurable and unmeasurable disturbances which can be regarded as the outputs of free systems with known dynamics. The unmeasurable disturbance must always be removed to achieve successful identification. When the disturbance is measurable, however, it does not have to be removed if it can provide a positive contribution to identification. A constant disturbance was shown to slow down the identification speed. The best results will be obtained if one selects a DDR which removes only undesirable disturbances. In this technical brief, discrete series-parallel and parallel identification schemes for single-input, single-output systems were considered. The same principle, however, can be extended to other situations including the continuous time case and multi-input, and multi-output case. References 1 Astrom, K. J., and Eykhoff, P., "System Identification -A Survey," Automatica, Vol. 7, 1971, pp. 123-16
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