Wide-Field InfraRed Survey Telescope (WFIRST) Final Report

In December 2010, NASA created a Science Definition Team (SDT) for WFIRST, the Wide Field Infra-Red Survey Telescope, recommended by the Astro 2010 Decadal Survey as the highest priority for a large space mission. The SDT was chartered to work with the WFIRST Project Office at GSFC and the Program Office at JPL to produce a Design Reference Mission (DRM) for WFIRST. Part of the original charge was to produce an interim design reference mission by mid-2011. That document was delivered to NASA and widely circulated within the astronomical community. In late 2011 the Astrophysics Division augmented its original charge, asking for two design reference missions. The first of these, DRM1, was to be a finalized version of the interim DRM, reducing overall mission costs where possible. The second of these, DRM2, was to identify and eliminate capabilities that overlapped with those of NASA's James Webb Space Telescope (henceforth JWST), ESA's Euclid mission, and the NSF's ground-based Large Synoptic Survey Telescope (henceforth LSST), and again to reduce overall mission cost, while staying faithful to NWNH. This report presents both DRM1 and DRM2.Comment: 102 pages, 57 figures, 17 table

Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms

This paper deals with the estimation of unknown signals in bioreactors using sliding observers. Particular attention is drawn to estimate the specific growth rate of microorganisms from measurement of biomass concentration. In a recent article, notions of high-order sliding modes have been used to derive a growth rate observer for batch processes. In this paper we generalize and refine these preliminary results. We develop a new observer with a different error structure to cope with other types of processes. Furthermore, we show that these observers are equivalent, under coordinate transformations and time scaling, to the classical super-twisting differentiator algorithm, thus inheriting all its distinctive features. The new observers’ family achieves convergence to timevarying unknown signals in finite time, and presents the best attainable estimation error order in the presence of noise. In addition, the observers are robust to modeling and parameter uncertainties since they are based on minimal assumptions on bioprocess dynamics. In addition, they have interesting applications in fault detection and monitoring. The observers performance in batch, fed-batch and continuous bioreactors is assessed by experimental data obtained from the fermentation of Saccharomyces Cerevisiae on glucose.This work was supported by the National University of La Plata (Project 2012-2015), the Agency for the Promotion of Science and Technology ANPCyT (PICT2007-00535) and the National Research Council CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain; and by the project FEDER of the European Union.De Battista, H.; Picó Marco, JA.; Garelli, F.; Navarro Herrero, JL. (2012). Reaction rate reconstruction from biomass concentration measurement in bioreactors using modified second-order sliding mode algorithms. Bioprocess and Biosystems Engineering. 35(9):1-11. https://doi.org/10.1007/s00449-012-0752-yS111359Aborhey S, Williamson D (1978) State amd parameter estimation of microbial growth process. Automatica 14:493–498Bastin G, Dochain D (1986) On-line estimation of microbial specific growth rates. Automatica 22:705–709Bastin G, Dochain D (1990) On-line estimation and adaptive control of bioreactors. Elsevier, AmsterdamBejarano F, Fridman L (2009) Unbounded unknown inputs estimation based on high-order sliding mode differentiator. In: Proceedings of the 48th IEEE conference on decision and control, pp 8393–8398Corless M, Tu J (1998) State and input estimation for a class of uncertain systems. Automatica 34(6):757–764Dabros M, Schler M, Marison I (2010) Simple control of specific growth rate in biotechnological fed-batch processes based on enhanced online measurements of biomass. Bioprocess Biosyst Eng 33:1109–1118Davila A, Moreno J, Fridman L (2010) Variable gains super-twisting algorithm: a lyapunov based design. In: American control conference (ACC), 2010, pp 968–973Dávila J, Fridman L, Levant A (2005) Second-order sliding-mode observer for mechanical systems. IEEE Transact Automatic Control 50(11):1785–1789De Battista H, Picó J, Garelli F, Vignoni A (2011) Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. J Process Control 21:1049–1055Dochain D (2001) Bioprocess control. Wiley, HobokenDochain D (2003) State and parameter estimation in chemical and biochemical processes: a tutorial. J Process Control 13(8):801–818Edwards C, Spurgeon S, Patton R (2000) Sliding mode observers for fault detection and isolation. 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Nonlinear Sliding Mode Observer Applied to Microalgae Growth

Modeling biological processes, such as algae growth, is an area of ongoing research. The ability to understand the multitude of parameters that influence this system provides a platform for better understanding the dynamics of microalgae growth. Empirical modeling efforts look to understand sources of driving nutrients that influence harmful algal blooms (HABs). These harmful algal blooms are dense aggregates that have an increasingly negative impact on local economics, marine and freshwater systems, and public health. They result from a high influx of nitrogen and nutrients that drive the algae biomass to exponentially grow. This growth blocks out the sun, potentially releases dangerous toxins, and suffocates marine life, damaging ecosystems, especially in Florida. Modeling microalgae behavior and growth is complex due to its nonlinear behavior and coupled variables. Recently, cultivating oleaginous microalgae for biofuel production has been another region of ongoing research, especially application of observer theory to estimate internal parameters that are not easily measured in algal systems. Linear observer theory has generally been applied to algae growth systems to estimate internal parameters that are beyond hardware sensor capabilities, but they are still severely limited. Nonlinear observer theory application to biological systems is still relatively new. This thesis explores the application of a nonlinear observer based off sliding mode to an algae system. Sliding mode is derived from modern control theory and is based off variable structure control. An algae system is modeled using the widely accepted Droop model for algae growth and a linear and nonlinear sliding mode observer is developed for the system to estimate internal nitrogen within the algae biomass

Robust Adaptive Sliding Mode Control Design with Genetic Algorithm for Brushless DC Motor

This study aims to design a control scheme that is capable to improve performance and efficiency of brushless DC motor (BLDC) in operating condition. The control scheme is composed of sliding mode controller (SMC) with proportional-integral-derivative (PID) sliding surface. The PID sliding surface is used to improve the system transient response. Then, the SMC-PID is optimized by genetic algorithm optimization for further improvement on the stability and robustness against nonlinearities and disturbances. Chattering problem that appear in the SMC is minimized by employing an adaptive switching gain for the SMC that is integrated with Luenberger Observer. Lyapunov function candidate is applied to guarantee the stability of the system. Simulation on the proposed work is done in Matlab Simulink. Results of the simulation works indicate that the proposed control scheme can improve the transient response, the stability and robustness of the BLDC motor compared to the conventional SMC in the existence of nonlinearities and disturbances

Recent Developments in Monitoring of Complex Population Systems

The paper is an update of two earlier review papers concerning the application of the methodology of mathematical systems theory to population ecology, a research line initiated two decades ago. At the beginning the research was con- centrated on basic qualitative properties of ecological models, such as observability and controllability. Observability is closely related to the monitoring problem of ecosystems, while controllability concerns both sustainable harvesting of population systems and equilibrium control of such systems, which is a major concern of conservation biology. For population system, observability means that, e.g. from partial observation of the system (observing only certain indica- tor species), in principle the whole state process can be recovered. Recently, for different ecosystems, the so-called ob- server systems (or state estimators) have been constructed that enable us to effectively estimate the whole state process from the observation. This technique offers an efficient methodology for monitoring of complex ecosystems (including spatially and stage-structured population systems). In this way, from the observation of a few indicator species the state of the whole complex system can be monitored, in particular certain abiotic effects such as environmental contamina- tion can be identified. In this review, with simple and transparent examples, three topics illustrate the recent develop- ments in monitoring methodology of ecological systems: stock estimation of a fish population with reserve area; and observer construction for two vertically structured population systems (verticum-type systems): a four-level ecological chain and a stage-structured fishery model with reserve area

Feedback Design for Devising Optimal Epidemic Control Policies

For reliable epidemic monitoring and control, this paper proposes a feedback mechanism design to effectively cope with data and model uncertainties. Using past epidemiological data, we describe methods to estimate the parameters of general epidemic models. Because the data could be noisy, the estimated parameters may not be accurate. Therefore, under uncertain parameters and noisy measurements, we provide an observer design method for robust state estimation. Then, using the estimated model and state, we devise optimal control policies by minimizing a predicted cost functional. Finally, the effectiveness of the proposed method is demonstrated through its implementation on a modified SIR epidemic model

Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers

[EN] This paper addresses the estimation of specific growth rate of microorganisms in bioreactors using sliding observers. In particular, a second-order sliding observer based on biomass concentration measurement is proposed. Differing from other proposals that only guarantee bounded errors, the proposed observer provides a smooth estimate that converges in finite time to the time-varying parameter. Stability is proved using a Lyapunov approach. The observer exhibits also robustness to process uncertainties since no model of the reaction is used for its design. In addition, the off-surface coordinate of the sliding observer is useful to determine the convergence time as well as to identify sensor faults and unexpected behaviors. Because of the structure of the output error injection, chattering phenomena of conventional sliding mode algorithms are substantially reduced. The features of the proposed observer are assessed by numerical and experimental data. (C) 2011 Elsevier Ltd. All rights reserved.This work was supported by the National University of La Plata (Project 11-1127), ANPCyT (PICT2007-00535) and CONICET (PIP112-200801-01052) of Argentina; the Technical University of Valencia (PAID-02-09 program and FPI-2009/21 grant), the CICYT (DPI2005-01180) and AECID (A/024186/09) of Spain: and by FEDER funds of the European Union.De Battista, H.; Picó, J.; Garelli, F.; Vignoni, A. (2011). Specific growth rate estimation in (fed-)batch bioreactors using second-order sliding observers. Journal of Process Control. 21(7):1049-1055. https://doi.org/10.1016/j.jprocont.2011.05.008S1049105521

Decentralization and public services : the case of immunization

The author studies the impact of political decentralization on childhood immunization, an essential public service provided in almost all countries. He examines the relationship empirically using a time-series data set of 140 low- and middle-income countries from 1980 to 1997. The author finds that decentralization has different effects in low- and middle-income countries. In the low-income group, decentralized countries have higher coverage rates than centralized ones, with an average difference of 8.5 percent for measles and DTP3 vaccines. In the middle-income group, the reverse effect is observed: decentralized countries have lower coverage rates than centralized ones, with an average difference of 5.2 percent for the same vaccines. Both results are significant at the 99 percent level. Modifiers of the decentralization-immunization relationship also differ in the two groups. In the low-income group, development assistance reduces the gains from decentralization. In the middle-income group, democratic government mitigates the negative effects of decentralization, and decentralization reverses the negative effects of ethnic tension and ethno-linguistic fractionalization, but institutional quality and literacy rates have no interactive effect either way. Similar results are obtained whether decentralization is measured with a dichotomous categorical variable or with more specific measures of fiscal decentralization. The study confirms predictions in the theoretical literature about the negative impact of local political control on services that have public goods characteristics and inter-jurisdictional externalities. The author discusses reasons for the difference between low- and middle-income countries.Public Health Promotion,Banks&Banking Reform,Health Monitoring&Evaluation,Decentralization,Municipal Financial Management,National Governance,Banks&Banking Reform,Municipal Financial Management,Health Monitoring&Evaluation,Governance Indicators