Publications Lists     [ all BibTeX ]  

In this paper, two methods to reduce the complexity of multi-parametric programming modelpredictive control are proposed. We show that the standard multi-parametric programming problem can be modified by approximating the quadratic programming constraints. For a certain controlsequence, only constraints on the first element is considered, while constraints on future elements are ignored or approximated to a simple saturation function. Both the number of critical regions and the computation time are proven to be reduced. Geometric interpretations is given and complexityanalysis is conducted. The result is tested on an illustrating example to show its effectiveness.

In  this  paper  we  address  the  average  consensus problem in a Wireless Sensor-Actor Network with the particular focus  on  autonomous  fault  detection.  To  this  aim,  we  design  a distributed  clustering  procedure  that  partitions  the  network into  clusters  according  to  both  similarity  of  measurements and communication connectivity. The exploitation of clustering techniques   in   consensus   computation   allows   to   obtain   the detection   and   isolation   of   faulty   nodes,   thus   assuring   the convergence  of  the  other  nodes  to  the  exact  consensus  value. More  interestingly,  the  algorithm  can  be  integrated  into  a Kalman filtering framework to perform distributed estimation of  a  dynamic  quantity  in  presence  of  faults.  The  proposed approach is validated through numerical simulations and tests on a real world scenario dataset.

IEEE 802.11 systems are drawing an ever increasing interest for wireless industrial communication, also thanks to the interesting features provided by the most recent and advanced amendments to this standard, such as IEEE 802.11n. Due to the intrinsic unreliability of the wireless medium, the current research efforts aim at improving both timeliness and reliability of such a protocol in view of its adoption for real-time applications. A significant issue in this context is represented by the reduction of the randomness that affects packet delivery times. An important benefit in this direction can be obtained by the deactivation of the standard legacy carrier sensing and backoff procedures. In this paper we show, through a simulative assessment, that a fine control of such features leads to improved real-time performance.

A new Bayesian approach to linear system identification
has been proposed in a series of recent papers. The
main idea is to frame linear system identification as predictor
estimation in an infinite dimensional space, with the aid of
regularization/Bayesian techniques. This approach guarantees
the identification of stable predictors based on the prediction
error minimization. Unluckily, the stability of the predictors
does not guarantee the stability of the impulse response of
the system. In this paper we propose and compare various
techniques to guarantee that the final model identified following
this Bayesian approach is stable. First, we consider the socalled
“LMI - constraint” approach and adapt it to constrain
the eigenvalues of the estimated model within the unit circle.
A second possibility which is being considered is to add to the
“classic” Stable-Spline algorithm a penalty term, depending on
the maximum absolute value of the eigenvalue of the system.
This last technique has the advantage of being integrated
directly inside the pre-existing optimization problem and not to
simply post-process the estimated model to guarantee stability.
Finally, we considered a Monte Carlo Markov Chain approach
sampling in both the space of hyper-parameters and of impulse
responses. Simulations results comparing these techniques will
be provided.

The IEEE 802.11 standard, since its earliest versions, provides the multi-rate support feature typically exploited by Rate Adaptation (RA) techniques to dynamically select the most suitable transmission rate, based on an estimation of the channel status. With the release of the IEEE 802.11n amendment, several enhancements have been introduced to the standard, notably the support for MIMO architectures, whose benefits can be effectively combined with multi-rate support. In an industrial communication scenario, the RA algorithms commonly available for general purpose applications revealed ineffective. This led to the definition of purposely designed algorithms, with the aim of improving the real-time behavior of IEEE 802.11 networks. In this paper we take into consideration these techniques, as well as some general purpose RA strategies, and analyze their implementation on an IEEE 802.11n communication system deployed in an industrial scenario. Furthermore, we propose an effective parameters tuning for the considered RA algorithms, as well as some enhancements conceived to enforce their timeliness. An exhaustive assessment, carried out via numerical simulations, shows that the improved techniques allow to achieve excellent performance.

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensionaland censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

In semiconductor manufacturing, wafer quality control strongly relies on product monitoring and physical metrology. However, the involved metrology operations, generally performed by means of scanning electron microscopes, are particularly cost-intensive and time-consuming. For this reason, in common practice a small subset of only a productive lot is measured at the metrology stations and it is devoted to represent the entire lot. Virtual Metrology (VM) methodologies are able to obtain reliable predictions of metrology results at process time, without actually performing physical measurements; this goal is usually achieved by means of statistical models and by linking process data and context information to target measurements. Since semiconductor manufacturing processes involve a high number of sequential operations, it is reasonable to assume that the quality features of a given wafer (such as layer thickness and critical dimensions) depend on the whole processing and not on the last step before measurement only. In this paper, we investigate the possibilities to enhance VM prediction accuracy by exploiting the knowledge collected from previous process steps. We present two different schemes of multi-step VM, along with dataset preparation indications; special consideration will be reserved to regression techniques capable of handling high-dimensional input spaces. The proposed multi-step approaches are tested on production data provided by a partner semiconductor manufacturing industry. 

In the paper output feedback control of Boolean control networks (BCNs) is investigated. First, necessary and sufficient
conditions for the existence of a time-invariant output feedback (TIOF) law, stabilizing the BCN to some equilibrium point,
are given, and constructive algorithms to test the existence of such a feedback law are proposed. Two sufficient conditions for
the existence of a stabilizing time-varying output feedback (TVOF) are then given. Finally, an example concerning the lac
Operon in the bacterium Escherichia Coli is presented, to illustrate the effectiveness of the proposed techniques.

Personal control with occupancy and daylight adaptation is considered in a lighting system with multiple luminaires. Each luminaire is equipped with a co-located occupancy sensor and light sensor that respectively provide local occupancy and illumination information to a central controller. Users may also provide control inputs to indicate a desired illuminance value. Using sensor feedback and user input, the central controller determines dimming values of the luminaires using an optimization framework. The cost function consists of a weighted sum of illumination errors at light sensors and the power consumption of the system. The optimum dimming values are determined with the constraints that the illuminance value at the light sensors are above the reference set-point at the light sensors and the dimming levels are within physical allowable limits. Different approaches to determine the set-points at light sensors associated with multiple user illumination requests are considered. The performance of the proposed constrained optimization problem is compared with a reference stand-alone controller under different simulation scenarios in an open-plan office lighting system.

Slow release drugs must be manufactured to meettarget speci?cations with respect to dissolution curve pro?les.In this paper we consider the problem of identifying thedrivers of dissolution curve variability of a drug from historicalmanufacturing data. Several data sources are considered: rawmaterial parameters, coating data, loss on drying and pellet sizestatistics. The methodology employed is to develop predictivemodels using LASSO, a powerful machine learning algorithmfor regression with high-dimensional datasets. LASSO providessparse solutions facilitating the identi?cation of the most importantcauses of variability in the drug fabrication process.The proposed methodology is illustrated using manufacturingdata for a slow release drug.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their effectiveness for a wide range of real-time industrial communication applications. Nonetheless, the enhancements at the PHY and MAC layers introduced by the IEEE 802.11n amendment have not yet been adequately addressed in the context of industrial communication. In this paper we investigate the impact of some IEEE 802.11n new features on some important performance figures for industrial applications, such as timeliness and reliability.