Advanced step nonlinear model predictive control for air separation units

Cryogenic air separation units constitute an integral part of many industrial processes and next generation power plants. These units are characterized by fluctuating operating conditions to respond to changing product demands. The dynamics of these transitions are highly nonlinear and energy-intensive. Consequently, nonlinear model predictive control (NMPC) based on rigorous dynamic models is essential for high performance in these applications. Currently, the implementation of NMPC controllers is limited by the computational complexity of the associated on-line optimization problems. In this work, we make use of the so-called advanced step NMPC controller to overcome these limitations. We demonstrate that this sensitivity-based strategy reduces the on-line computational time to just a single CPU second, while incorporating a highly detailed dynamic air separation unit model. Finally, we demonstrate that the controller can handle nonlinear dynamics over a wide range of operating conditions.     

Network Elucidation Template: A framework for human-guided network inference

Network elucidation is the problem of inferring all parameters of a network from a subset of those parameters. We introduce the Network Elucidation Template (NET), which provides a framework upon which algorithms for such problems can be built. NET algorithms take advantage of novel methods for collaboration between human operators and computers. They use visualizations of the peculiar structures that appear in optimal solutions to aid the parameter search. By design, NET is at a high enough level of abstraction to describe a class of algorithms, as opposed to a single algorithm. Given a problem, and the structure of that problem, an effective instantiation of the template into an algorithm can be created. We describe one such instantiation: using a network flow framework to implement a NET algorithm for uncovering smuggling networks; as well as the general template.     

Robust path tracking using flatness for fractional linear MIMO systems: A thermal application

This paper deals with robust path tracking using flatness principles extended to fractional linear MIMO systems. As soon as the path has been obtained by means of the fractional flatness, a robust path tracking based on CRONE control is presented. Flatness in path planning is used to determine the controls to apply without integrating any differential equations when the trajectory is fixed (in space and in time). Several developments have been made for fractional linear SISO systems using a transfer function approach. For fractional systems, especially in MIMO, developments are still to be made. Throughout this paper, flatness principles are applied using polynomial matrices for fractional linear MIMO systems. To illustrate the robustness performances, a third-generation multi-scalar CRONE controller is compared to a PID one.     

A PD path-tracking controller plus inner velocity loops for a wheeled mobile robot

In this paper, we present a controller to solve the path-tracking task in a wheeled mobile robot. We show that a linear PD controller, driven by the tracking error, can be used to generate the desired profiles to be tracked by the motor velocities by means of two linear inner proportional-integral loops. We formally prove that an ultimate bound exists which can be rendered small by a suitable selection of the controller gains. This is the first time that such a result is presented in the literature when considering, simultaneously, both the kinematic and the dynamic models of the wheeled mobile robot.     

An interval-valued reliability model with bounded failure rates

The approach to deriving interval-valued reliability measures described in this paper is distinctive from other imprecise reliability models in that it overcomes the issue of having to impose an upper bound on time to failure. It rests on the presupposition that a constant interval-valued failure rate is known possibly along with other reliability measures, precise or imprecise. The Lagrange method is used to solve the constrained optimization problem to derive new reliability measures of interest. The obtained results call for an exponential-wise approximation of failure probability density function if only partial failure information is available. An example is provided.     

Automatic evaluation of nickel alloy secondary phases from SEM images

Quantitative metallography is a technique to determine and correlate the microstructures of materials with their properties and behavior. Generic commercial image processing and analysis software packages have been used to quantify material phases from metallographic images. However, these all-purpose solutions also have some drawbacks, particularly when applied to segmentation of material phases. To overcome such limitations, this work presents a new solution to automatically segment and quantify material phases from SEM metallographic images. The solution is based on a neuronal network and in this work was used to identify the secondary phase precipitated in the gamma matrix of a Nickel base alloy. The results obtained by the new solution were validated by visual inspection and compared with the ones obtained by a commonly used commercial software. The conclusion is that the new solution is precise, reliable and more accurate and faster than the commercial software.     

Using quantitative models to search for appropriate organizational designs

As the scale and scope of distributed and multi-agent systems grow, it becomes increasingly important to design and manage the participants’ interactions. The potential for bottlenecks, intractably large sets of coordination partners, and shared bounded resources can make individual and high-level goals difficult to achieve. To address these problems, many large systems employ an additional layer of structuring, known as an organizational design, that assigns agents different roles, responsibilities and peers. These additional constraints can allow agents to operate more efficiently within the system by limiting the options they must consider. Different designs applied to the same problem will have different performance characteristics, therefore it is important to understand the behavior of competing candidate designs. In this article, we describe a new representation for capturing such designs, and in particular we show how quantitative information can form the basis of a flexible, predictive organizational model. The representation is capable of capturing a wide range of multi-agent characteristics in a single, succinct model. We demonstrate the language’s capabilities and efficacy by comparing a range of metrics predicted by detailed models of a distributed sensor network and information retrieval system to empirical results. These same models also describe the space of possible organizations in those domains and several search techniques are described that can be used to explore this space, using those quantitative predictions and context-specific definitions of utility to evaluate alternatives. The results of such a search process can be used to select the organizational design most appropriate for a given situation. This material is based upon work supported in part by the National Science Foundation Engineering Research Centers Program under NSF Award No. EEC-0313747. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Effort also sponsored in part by the Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory Air Force Materiel Command, USAF, under agreement number F30602-99-2-0525. The US Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory or the US Government.     

Power calculations for generalized linear models in observational longitudinal studies: a simulation approach in SAS

Repeated measurements arising from longitudinal studies occur frequently in applied research. Methods to calculate power in the context of repeated measures are available for experimental settings where the covariate of interest is a discrete treatment indicator. However, no closed form expression exists to calculate power for generalized linear models with non-zero within-cluster correlation that are common in epidemiological and observational studies in which the covariate of interest varies over time and is often measured on a continuous scale, and where the researchers control for several potential confounders. We describe a Monte Carlo simulation approach conducted to calculate power, and illustrate its application in two models frequently encountered in practice, the normal linear mixed model, and the logistic regression model, both with repeated measurements and non-zero within-cluster correlation. This approach can be used to calculate the effect on power of changing various simulation conditions controlled by the researcher, such as sample size, within-cluster correlation structure, smallest meaningful difference to detect, and distributional assumptions.