A minimum-time control strategy for torque tracking in permanent magnet AC motor drives

A minimum-time torque control strategy for permanent-magnet AC motor drives is presented. The proposed technique neither requires the solution of a HJB-type equation, which would be practically unfeasible, nor uses Pontryagin's maximum principle. Instead, the solution is obtained by an ad hoc procedure based on the computation of reachability and controllability sets. In principle, the optimal control strategy can be carried out by iteratively solving a fourth-degree polynomial equation. For its efficient implementation, an algorithm based on Sturm sequences is suggested. The sequence of online operations required by the algorithm for a given tolerance on the optimal time is illustrated. The method has been tested on a laboratory prototype. Experimental results show the effectiveness of the technique.     

An Optical Thermometer Exploiting Periodically Poled Lithium Niobate for Monitoring the Pantographs of High-Speed Trains

Optical thermometers have been widely investigated. Here, the temperature behavior of second harmonic generation (SHG) in periodically poled lithium niobate (PPLN) substrates is analyzed; indeed, the QPM tuning in PPLN devices and the obtained SHG efficiency depend on the crystal thermal expansion and dispersion, particularly in the case of guided propagation. Therefore, such devices are suitable to realize optical thermometers for demanding applications. This investigation originated with the request of a thermometer to be installed on the pantographs of high-speed trains. Therefore, it must be sturdy and reliable, but it has even to work in an EMD environment. The temperature behavior of the SHG was theoretically modeled and experimentally validated at 1550 nm, in both bulk propagation and APE channel waveguides. In the first case, by using a 10-mW source, which was obtained from a laser diode and a fiber amplifier, an accuracy of 0.3 degC was found. The pump power was about three orders of magnitude smaller in guided propagation. In view of testing on the trains, our investigation resulted in the design of a device without mechanical contacts with the input and output fibers. Since it works in free propagation, there are no serious alignment and packaging problems. The performances, which are expected to be the same of our tests, widely satisfy all the requirements for working effectively in a strongly hostile and EMD environment and for giving accurate measurements on a wide range of temperatures     

First data from the EGLE experiment onboard the ISS

EGLE is a wide frequency band search- coil magnetometer designed and built at the Roma Tre University. It has been installed onboard the ISS by the Italian astronaut Roberto Vittori on April 25, 2005 within the LAZIO- EGLE experiment carried out during the ENEIDE Soyuz mission. The scope of the experiment is to test EGLE in space and to investigate geomagnetic field variations. The main applications of EGLE are the study of electromagnetic environment inside the ISS, the correlation of magnetic field data with particle fluxes detected by LAZIO particle detector, and the monitoring of ionospheric perturbations possibly caused by Earth seismic activity. Since continuous electromagnetic field measurements on board the ISS are important for diverse space applications, a magnetometer with a suitable design is requested. Appropriate solutions for these applications, which have been adopted by EGLE, are in particular the use of 1- Wire technology and the possibility to detect by means of a search- coil magnetometer a large portion of the ULF frequency band, usually measured by flux- gate probes. To investigate the topside ionosphere electromagnetic environment and stability of Van Allen radiation belts in relation with seismic and anthropogenic electromagnetic emissions, a specific satellite mission (the ESPERIA project) has been designed for the Italian Space Agency (ASI), and up to now a few instruments of its payload have been built and tested in space. One of them is exactly the EGLE search- coil magnetometer. The first magnetic observations performed by this instrument reveal to be promising and demand for a further and deeper analysis based on a longer time series of data.     

Rocket seedling production on the international space station: Growth and nutritional properties

Producing sprouts directly during space missions may represent an interesting opportunity to offer high-quality fresh ready to eat food to the astronauts. The goal of this work was to compare, in terms of growth and nutritional quality, rocket (Eruca sativa Mill.) seedlings grown in the International Space Station during the ENEIDE mission with those grown in a ground-based experiment (in presence and absence of clinorotation). The rocket seedlings obtained from the space-experiment were thinner and more elongated than those obtained in the ground-based experiment. Cotyledons were often closed in the seedlings grown in the space experiment. Quantitative (germination, fresh and dry weight) and qualitative (glucose, fructose, sucrose and starch) traits of rocket seedling were negatively affected by micrograv-ity, especially those recorded on seedlings grown under real microgravity conditions The total chlorophyll, and carotenoids of seedlings obtained in the space experiment were strongly reduced in comparison to those obtained in the ground-based experiment (presence and absence of clinorotation). The results showed that it is possible to produce rocket seedlings in the ISS; however, further studies are needed to define the optimal environmental conditions for producing rocket seedlings with high nutritional value     

Using micro-documents for feature selection: The case of ordinal text classification

Most popular feature selection methods for text classification such as information gain (also known as “mutual information”), chi-square, and odds ratio, are based on binary information indicating the presence/absence of the feature (or “term”) in each training document. As such, these methods do not exploit a rich source of information, namely, the information concerning how frequently the feature occurs in the training document (term frequency). In order to overcome this drawback, when doing feature selection we logically break down each training document of length k into k training “micro-documents”, each consisting of a single word occurrence and endowed with the same class information of the original training document. This move has the double effect of (a) allowing all the original feature selection methods based on binary information to be still straightforwardly applicable, and (b) making them sensitive to term frequency information. We study the impact of this strategy in the case of ordinal text classification, a type of text classification dealing with classes lying on an ordinal scale, and recently made popular by applications in customer relationship management, market research, and Web 2.0 mining. We run experiments using four recently introduced feature selection functions, two learning methods of the support vector machines family, and two large datasets of product reviews. The experiments show that the use of this strategy substantially improves the accuracy of ordinal text classification.     

Lyapunov based predictive control of vehicle drivetrains over CAN

Vehicle drivetrains are characterized by fast dynamics, subject to physical and control constraints, which make controller design for driveline oscillations damping a challenging problem. Furthermore, in current implementations, the connections between the controller and the physical plant are realized using a controller area network (CAN) as the communication medium, which introduces time-varying delays. As such, the goal of this paper is to provide a control design methodology that can cope with all these challenges and limitations and still yield an effective solution. To this end, firstly, a continuous-time model of a vehicle drivetrain is derived. Then, a method for determining a worst case upper bound on the delays that can be introduced by a CAN is presented, which enables the usage of a polytopic approximation technique to obtain a discrete-time model of the closed-loop CAN system. Thirdly, a non-conservative Lyapunov based predictive controller is designed for the resulting model with time-varying delays, polytopic uncertainty and hard constraints. Several tests performed using an industry validated drivetrain model and the Matlab toolbox TrueTime indicate that the proposed design methodology can handle both the performance/physical constraints and the strict limitations on the computational complexity, while effectively coping with time-varying delays. Preliminary real-time results further validate the proposed methodology.