An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.     

Adaptive control of linear time-varying systems

Single-input single-output uncertain linear time-varying systems are considered, which are affected by unknown bounded additive disturbances; the uncertain time-varying parameters are required to be smooth and bounded but are neither required to be sufficiently slow nor to have known bounds. The output, which is the only measured variable, is required to track a given smooth bounded reference trajectory. The undisturbed system is assumed to be minimum-phase and to have known and constant relative degree, known sign of the ‘high frequency gain’, known upper bound on the system order. An adaptive output feedback control algorithm is designed which assures: (i) boundedness of all closed-loop signals; (ii) arbitrarily improved transient performance of the tracking error; (iii) asymptotically vanishing tracking error when parameter time derivatives are L₁ signals and disturbances are L₂ signals.     

Retrieval of leaf area index in different vegetation types using high resolution satellite data

With the successful launch of the IKONOS satellite, very high geometric resolution imagery is within reach of civilian users. In the 1-m spatial resolution images acquired by the IKONOS satellite, details of buildings, individual trees, and vegetation structural variations are detectable. The visibility of such details opens up many new applications, which require the use of geometrical information contained in the images. This paper presents an application in which spectral and textural information is used for mapping the leaf area index (LAI) of different vegetation types. This study includes the estimation of LAI by different spectral vegetation indices (SVIs) combined with image textural information and geostatistical parameters derived from high resolution satellite data. It is shown that the relationships between spectral vegetation indices and biophysical parameters should be developed separately for each vegetation type, and that the combination of the texture indices and vegetation indices results in an improved fit of the regression equation for most vegetation types when compared with one derived from SVIs alone. High within-field spatial variability was found in LAI, suggesting that high resolution mapping of LAI may be relevant to the introduction of precision farming techniques in the agricultural management strategies of the investigated area.     

Time-optimal velocity planning along predefined path for static formations of mobile robots

International Journal of Control, Automation and Systems - This paper is concerned with the problem of finding a time-optimal velocity profile along the predefined path for static formations of...     

Model-based cold-start speed control scheme for spark ignition engines

This paper presents a model-based control scheme to the cold-start speed control in spark ignition (SI) engines. The multi-variable control algorithm is developed with the purpose of improving the transient performance of the starting engine speed: the control inputs are the fuel injection, the throttle and the spark advance (SA), while the engine speed and the air mass flow rate are the measured signals. The fuel injection is performed with a dual sampling rate system: the cycle-based fuel injection command is individually adjusted for each cylinder by using a TDC (top dead center)-based air charge estimation. The desired performance for speed regulation is achieved by using a coordinated control of SA and throttle operation. The speed error convergence of the closed loop system is proved for simplified, second-order model with a time-delay, and the robustness with respect to parameter uncertainties is investigated. The performance and the robustness with respect to modeling uncertainties of the proposed control scheme are tested using an industrial engine simulator with six cylinders.     

Training visual attention in a naturalistic visual environment

The efficiency of training visual attention in the central and peripheral visual field was investigated by means of a visual detection task that was performed in a naturalistic visual environment including numerous, time-varying visual distractors. We investigated the minimum number of repetitions of the training required to obtain the top performance and whether intra-day training improved performance as efficiently as inter-day training. Additionally, our research aimed to find out whether exposure to a demanding task such as a microsurgical intervention may cancel out the effects of training.

Results showed that performance in visual attention peaked within three (for tasks in the central visual field) to seven (for tasks in the periphery) days subsequent to training. Intra-day training had no significant effect on performance. When attention training was administered after exposure to stress, improvement of attentional performance was more pronounced than when training was completed before the exposure. Our findings support the implementation of training in situ at work for more efficient results.

Practitioner Summary: Visual attention is important in an increasing number of workplaces, such as with surveillance, inspection, or driving. This study shows that it is possible to train visual attention efficiently within three to seven days. Because our study was executed in a naturalistic environment, training results are more likely to reflect the effects in the real workplace.     

Co-precipitated RuO2-Al2O3 catalysts: bulk and surface characterization

A series of co-precipitated RuO₂-Al₂O₃ samples was characterized by means of bulk and surface techniques such as X-ray diffraction (XRD), specific surface area measurements, X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES). The existence of a substitutional solid solution of Al³⁺ ions in RuO₂ is suggested on the basis of XRD results. A more detailed study of such a phase was hindered by its thermal instability. XPS and AES quantitative data indicate a strong enrichment of Al on the surface. A simple model based on a reciprocal masking action of the particles of the two oxides with respect to the primary beam (X-rays or electrons) was found to fit the surface composition data well.     

A study on the stability of superconducting YBa2Cu3O7−x phase

The thermal stability of the superconducting phase of nominal composition YBa₂Cu₃O_7–x -sintered pellets has been studied with respect to different temperatures (ranging from 300 to 950° C), time (ranging from 1 to 72 h), oxygen partial pressure (from 4 Pa to 1 atm) and carbon dioxide partial pressure (from 10^–4 Pa to 1 atm). Annealed samples were characterized by X-ray diffraction analysis, optical microscopy, and resistive measurements of the superconductive transition temperature. A stability field of the orthorhombic and tetragonal phases was obtained, showing a region of coexistance. The decomposition of the 1 2 3 phase is found to be strongly influenced by the presence of a small amount of CO₂ (1 p.p.m.) in the sintering atmosphere. A sintering process is proposed to avoid the formation of by-products.     

Instrumental technique evolution for olive oil sensory analysis

At present, two systems have been usually used to identify olive oil aroma: the official panel test, according to the European Union Regulation [1], and the gas chromatographic method and its improvements. However, both types of techniques have two principal disadvantages: They need a long time for analysis and cannot be applied on‐line. Recently, there has been increasing interest in the development of a new device, the so‐called “electronic nose”. The aim of this work is to perform both a review of these techniques used for olive oil sensory analysis and their advantages and disadvantages.