Distributed non-cooperative robust economic predictive control for dynamically coupled linear systems.

In this thesis, a tube-based Distributed Economic Predictive Control (DEPC) scheme is presented for a group of dynamically coupled linear subsystems. These subsystems are components of a large scale system and control inputs are computed based on optimizing a local economic objective. Each subsystem is interacting with its neighbors by sending its future reference trajectory, at each sampling time. It solves a local optimization problem in parallel, based on the received future reference trajectories of the other subsystems. To ensure recursive feasibility and a performance bound, each subsystem is constrained to not deviate too much from its communicated reference trajectory. This difference between the plan trajectory and the communicated one is interpreted as a disturbance on the local level. Then, to ensure the satisfaction of both state and input constraints, they are tightened by considering explicitly the effect of these local disturbances. The proposed approach averages over all possible disturbances, handles tightened state and input constraints, while satisfies the compatibility constraints to guarantee that the actual trajectory lies within a certain bound in the neighborhood of the reference one. Each subsystem is optimizing a local arbitrary economic objective function in parallel while considering a local terminal constraint to guarantee recursive feasibility. In this framework, economic performance guarantees for a tube-based distributed predictive control (DPC) scheme are developed rigorously. It is presented that the closed-loop nominal subsystem has a robust average performance bound locally which is no worse than that of a local robust steady state. Since a robust algorithm is applying on the states of the real (with disturbances) subsystems, this bound can be interpreted as an average performance result for the real closed-loop system. To this end, we present our outcomes on local and global performance, illustrated by a numerical example.

Laser cladding and its potential to reduce particulate matter emissions from the automotive and locomotive sector

Laser Cladding (LC) is an emerging technology which is used both for coating applications as well as near-net shape fabrication. Despite its significant advantages, such as low dilution and metallurgical bond with the substrate, it still faces issues such as process control and repeatability, which restricts the extension to its applications. The following thesis evaluates the LC technology and tests its potential to be applied to reduce particulate matter emissions from the automotive and locomotive sector. The evaluation of LC technology was carried out for the deposition of multi-layer and multi-track coatings. 316L stainless steel coatings were deposited to study the minimisation of geometric distortions in thin-walled samples. Laser power, as well as scan strategy, were the main variables to achieve this goal. The use of constant power, reduction at successive layers, a control loop control system, and two different scan strategies were studied. The closed-loop control system was found to be practical only when coupled with the correct scan strategy for the deposition of thin walls. Three overlapped layers of aluminium bronze were deposited onto a structural steel pipe for multitrack coatings. The effect of laser power, scan speed and hatch distance on the final geometry of coating were studied independently, and a combined parameter was established to effectively control each geometrical characteristic (clad width, clad height and percentage of dilution). LC was then applied to coat commercial GCI brake discs with tool steel. The optical micrography showed that even with preheating, the cracks that originated from the substrate towards the coating were still present. The commercial brake discs emitted airborne particles whose concentration and size depended on the test conditions used for simulation in the laboratory. The contact of LC cladded wheel with rail emitted significantly less ultra-fine particles while maintaining the acceptable values of coefficient of friction.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

Development and Automatization of a Planar Jet Wind Tunnel for the X-wire Calibration

The scope of this study is to design an automatic control system and create an automatic x-wire calibrator for a facility named Plane Air Tunnel; whose exit creates planar jet flow. The controlling power state as well as automatic speed adjustment of the inverter has been achieved. Thus, the wind tunnel can be run with respect to any desired speed and the x-wire can automatically be calibrated at that speed. To achieve that, VI programming using the LabView environment was learned, to acquire the pressure and temperature, and to calculate the velocity based on the acquisition data thanks to a pitot-static tube. Furthermore, communication with the inverter to give the commands for power on/off and speed control was also done using the LabView VI coding environment. The connection of the computer to the inverter was achieved by the proper cabling using DAQmx Analog/Digital (A/D) input/output (I/O). Moreover, the pressure profile along the streamwise direction of the plane air tunnel was studied. Pressure tappings and a multichannel pressure scanner were used to acquire the pressure values at different locations. Thanks to that, the aerodynamic efficiency of the contraction ratio was observed, and the pressure behavior was related to the velocity at the exit section. Furthermore, the control of the speed was accomplished by implementing a closed-loop PI controller on the LabView environment with and without using a pitot-static tube thanks to the pressure behavior information. The responses of the two controllers were analyzed and commented on by giving suggestions. In addition, hot wire experiments were performed to calibrate automatically and investigate the velocity profile of a turbulent planar jet. To be able to analyze the results, the physics of turbulent planar jet flow was studied. The fundamental terms, the methods used in the derivation of the equations, velocity profile, shear stress behavior, and the effect of vorticity were reviewed.

The closed-string 3-loop amplitude and S-duality

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The surface accessibility of the glycine receptor M2-M3 loop is increased in the channel open state

Mutations in the extracellular M2-M3 loop of the glycine receptor (GlyR) alpha1 subunit have been shown previously to affect channel gating. In this study, the substituted cysteine accessibility method was used to investigate whether a structural rearrangement of the M2-M3 loop accompanies GlyR activation. All residues from R271C to V277C were covalently modified by both positively charged methanethiosulfonate ethyltrimethylammonium (MTSET) and negatively charged methanethiosulfonate ethylsulfonate (MTSES), implying that these residues form an irregular surface loop. The MTSET modification rate of all residues from R271C to K276C was faster in the glycine-bound state than in the unliganded state. MTSES modification of A272C, L274C, and V277C was also faster in the glycine-bound state. These results demonstrate that the surface accessibility of the M2-M3 loop is increased as the channel transitions from the closed to the open state, implying that either the loop itself or an overlying domain moves during channel activation.

Symmetric periodic orbits near a heteroclinic loop in R3 formed by two singular points, a semistable periodic orbit and their invariant manifolds

In this paper we consider C1 vector fields X in R3 having a “generalized heteroclinic loop” L which is topologically homeomorphic to the union of a 2–dimensional sphere S2 and a diameter connecting the north with the south pole. The north pole is an attractor on S2 and a repeller on . The equator of the sphere is a periodic orbit unstable in the north hemisphere and stable in the south one. The full space is topologically homeomorphic to the closed ball having as boundary the sphere S2. We also assume that the flow of X is invariant under a topological straight line symmetry on the equator plane of the ball. For each n ∈ N, by means of a convenient Poincar´e map, we prove the existence of infinitely many symmetric periodic orbits of X near L that gives n turns around L in a period. We also exhibit a class of polynomial vector fields of degree 4 in R3 satisfying this dynamics.

Arabidopsis Basic Helix-Loop-Helix Transcription Factors MYC2, MYC3, and MYC4 Regulate Glucosinolate Biosynthesis, Insect Performance, and Feeding Behavior.

Arabidopsis thaliana plants fend off insect attack by constitutive and inducible production of toxic metabolites, such as glucosinolates (GSs). A triple mutant lacking MYC2, MYC3, and MYC4, three basic helix-loop-helix transcription factors that are known to additively control jasmonate-related defense responses, was shown to have a highly reduced expression of GS biosynthesis genes. The myc2 myc3 myc4 (myc234) triple mutant was almost completely devoid of GS and was extremely susceptible to the generalist herbivore Spodoptera littoralis. On the contrary, the specialist Pieris brassicae was unaffected by the presence of GS and preferred to feed on wild-type plants. In addition, lack of GS in myc234 drastically modified S. littoralis feeding behavior. Surprisingly, the expression of MYB factors known to regulate GS biosynthesis genes was not altered in myc234, suggesting that MYC2/MYC3/MYC4 are necessary for direct transcriptional activation of GS biosynthesis genes. To support this, chromatin immunoprecipitation analysis showed that MYC2 binds directly to the promoter of several GS biosynthesis genes in vivo. Furthermore, yeast two-hybrid and pull-down experiments indicated that MYC2/MYC3/MYC4 interact directly with GS-related MYBs. This specific MYC-MYB interaction plays a crucial role in the regulation of defense secondary metabolite production and underlines the importance of GS in shaping plant interactions with adapted and nonadapted herbivores.

Assessment of the levels of degradation in fat co- and by-products for feed uses and their relationships with some lipid composition parameters

This paper discusses the levels of degradation of some co- and byproducts of the food chain intended for feed uses. As the first part of a research project, 'Feeding Fats Safety', financed by the sixth Framework Programme-EC, a total of 123 samples were collected from 10 European countries, corresponding to fat co- and byproducts such as animal fats, fish oils, acid oils from refining, recycled cooking oils, and other. Several composition and degradation parameters (moisture, acid value, diacylglycerols and monoacylglycerols, peroxides, secondary oxidation products, polymers of triacylglycerols, fatty acid composition, tocopherols, and tocotrienols) were evaluated. These findings led to the conclusion that some fat by- and coproducts, such as fish oils, lecithins, and acid oils, show poor, nonstandardized quality and that production processes need to be greatly improved. Conclusions are also put forward about the applicability and utility of each analytical parameter for characterization and quality control.

M-estimator, and D-optimality model construction using orthogonal forward regression

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

Biotic carbon feedbacks in a materially-closed soil-vegetation-atmosphere system

The magnitude and direction of the coupled feedbacks between the biotic and abiotic components of the terrestrial carbon cycle is a major source of uncertainty in coupled climate–carbon-cycle models1, 2, 3. Materially closed, energetically open biological systems continuously and simultaneously allow the two-way feedback loop between the biotic and abiotic components to take place4, 5, 6, 7, but so far have not been used to their full potential in ecological research, owing to the challenge of achieving sustainable model systems6, 7. We show that using materially closed soil–vegetation–atmosphere systems with pro rata carbon amounts for the main terrestrial carbon pools enables the establishment of conditions that balance plant carbon assimilation, and autotrophic and heterotrophic respiration fluxes over periods suitable to investigate short-term biotic carbon feedbacks. Using this approach, we tested an alternative way of assessing the impact of increased CO2 and temperature on biotic carbon feedbacks. The results show that without nutrient and water limitations, the short-term biotic responses could potentially buffer a temperature increase of 2.3 °C without significant positive feedbacks to atmospheric CO2. We argue that such closed-system research represents an important test-bed platform for model validation and parameterization of plant and soil biotic responses to environmental changes.

Dynamic simulation for closed loops in heating systems with decision-making support

Nowadays utilising the proper HVAC system is essential both in extreme weather conditions and dense buildings design. Hydraulic loops are the most common parts in all air conditioning systems. This article aims to investigate the performance of different hydraulic loop arrangements in variable flow systems. Technical, economic and environmental assessments have been considered in this process. A dynamic system simulation is generated to evaluate the system performance and an economic evaluation is conducted by whole life cost assessment. Moreover, environmental impacts have been studied by considering the whole life energy consumption, CO2 emission, the embodied energy and embodied CO2 of the system components. Finally, decision-making in choosing the most suitable hydraulic system among five well-known alternatives has been proposed.

Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

The long wavelength limit of hard thermal loop effective actions

We derive a closed form expression for the long wavelength limit of the effective action for hard thermal loops in an external gravitational field. It is a function of the metric, independent of time derivatives. It is compared and contrasted with the static limit, and with the corresponding limits in an external Yang-Mills field. (C) 2009 Elsevier B.V. All rights reserved.

Optimization of fibrolytic enzyme production by Aspergillus japonicus C03 with potential application in ruminant feed and their effects on tropical forages hydrolysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)