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In automation systems, Proportional-Integral-Derivative (PID) controllers are extensively used for their simplicity, reliability, and ability to maintain desired process levels by continuously adjusting control inputs to minimize the error between the set-point and the actual process variable. Despite the advantages, they have to face challenges such as handling non-linearities, sensitivity to parameter tuning, limited adaptability to changing process dynamics, and susceptibility to disturbances. For these reasons, various advanced control strategies and adaptive algorithms based on deep neural network are being developed and implemented. The aim of the proposed study is to introduce an innovative method for the regulation of processes, using an ESN-based deep neural network model. As preliminary results a case study on a heating system is considered. The model was trained using data generated under the control of a PID controller and tested on a using a TCLab module. The results indicate that the trained model effectively predicts the outputs of the heating system and the corresponding temperature values in alignment with the target temperatures. This study would be a starting point for a future deep methodological exploration of the mathematical aspects of ESN models to ensure the long-term stability of the system and its limitations. [ABSTRACT FROM AUTHOR]

Abstract: Complex processes are naturally suitable to be controlled in a decentralized framework: centralized control solutions are often unfeasible in dealing with large scale plants and they are computationally prohibitive when the processes are too fast for the existing computational resources. In these cases, the resulting control problem is usually split into many smaller subproblems and the global requirements are guaranteed by means of a proper coordination. A coordination strategy based on a networked decentralized Model Predictive Control is proposed in this paper for improving the global control performances. The innovative solution is based on independent agents and on a local area network used for exchanging a reduced set of information. The proposed architecture guarantees satisfactory performance under strong interactions among subsystems. A stability analysis is presented for the unconstrained decentralized case and the provided stability results are employed for tuning the decentralized controller. Numerical simulations are given for testing and validating the proposed technique. [Copyright &y& Elsevier]

One of the most critical issues in Ambient Assisted Living (AAL) is the design of systems that can evolve to meet the requirements of individuals as their needs and health conditions change. Although much work has been done on home and building automation systems for AAL, often referred to as assistive domotics, there is in fact still a substantial lack of solutions capable to support system designers in the early stage of development of such assistive systems. To this aim, the work contributes to the research on design of assistive domotic systems by presenting an ontology-driven methodology aimed to guide the development process. The novel contributions of the paper include the goal-oriented approach of the methodology, which involves the elicitation and analysis of AAL requirements and their formal representation in an ontology, where high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. Moreover, logic-based reasoning enables more advanced functionalities useful at design time. We present a validation of the methodology showing typical use cases both related to the development from scratch of a domotic system with assistive capabilities starting from a set of high-level user requirements and the redesign of existing implementations according to changed requirements. [ABSTRACT FROM AUTHOR]

The paper presents a solution to guarantee a fault-tolerant robust control for the dynamic positioning of an over-actuated offshore supply vessel. Fault detection is obtained by a combination of two model-based techniques: the parity space approach and the Luenberger observer. The dynamic positioning system is provided by a bank of reconfigurable Discrete-Time Variable-Structure Controllers (DTVSC), selected by a supervisor, based on a fault isolation logic. The control system is combined with a wave compensation based on a Multi-rate Extended Kalman Filter (MREKF). The proposed solution is compared with a standard Proportional-Integral-Derivative (PID) control system and a passive nonlinear wave filter to assess its robustness to input disturbances and uncertainties in the model parameters. The simulation tests developed for a scale model of an offshore supply vessel show that, in the case of actuators faults, dynamic positioning is guaranteed by the proposed solution. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a computational procedure designed to derive a regional model of the low temperature geothermal potential and its economic exploitability. The model was applied to the Italian Marche region and developed with the support of a Geographic Information System (GIS), which highlights the spatial dependencies in the distribution of geothermic resources. The Low Temperature Geothermal Energy has already gained attention as a renewable energy resource for domestic heating and represents a growing opportunity for investment. Although it is common practice to conduct an accurate evaluation of the geothermal potential and its exploitability on a site during the construction of a single installation, there is not an established practice or guidelines for estimating this resource over large territories. This information could support an institution’s ability to conduct regional energy planning and guide private entrepreneurship to meet new economic opportunities. To address these issues, the main contribution of this work is a model that reduces the distance between the physical knowledge of the territory/environment and economic analysis. The model is based on a useful assessment of low temperature geothermal potential obtained from physical parameters on a regional scale, from which a set of economic indicators are calculated to evaluate the actual economic accessibility to the geothermal energy resource. [Copyright &y& Elsevier]

Abstract: In this paper the Simultaneous Localization And Mapping (SLAM) problem in unknown indoor environments is addressed. A probabilistic approach integrating FastSLAM algorithm and a line feature map is developed and validated. Experimental validation is performed by a smart wheelchair equipped with proprioceptive and exteroceptive sensors in an office like environment where loop closing is achieved without any dedicated algorithm. Geometric hypotheses of orthogonal line features are considered to enhance the performance of the algorithm in the considered environment. The proposed approach results in a computationally efficient solution to the SLAM problem and the high quality sensor measurements allow to maintain a good localization of the mobile base and a compact representation of the environment. [Copyright &y& Elsevier]

This work proposes a real-time monitoring tool aimed to support clinicians for remote assessing exercise performances during home-based rehabilitation. The study relies on clinician indications to define kinematic features, that describe five motor tasks (i.e., the lateral tilt of the trunk, lifting of the arms, trunk rotation, pelvis rotation, squatting) usually adopted in the rehabilitation program for axial disorders. These features are extracted by the Kinect v2 skeleton tracking system and elaborated to return disaggregated scores, representing a measure of subjects performance. A bell-shaped function is used to rank the patient performances and to provide the scores. The proposed rehabilitation tool has been tested on 28 healthy subjects and on 29 patients suffering from different neurological and orthopedic diseases. The reliability of the study has been performed through a cross-sectional controlled design methodology, comparing algorithm scores with respect to blinded judgment provided by clinicians through filling a specific questionnaire. The use of task-specific features and the comparison between the clinical evaluation and the score provided by the instrumental approach constitute the novelty of the study. The proposed methodology is reliable for measuring subject’s performance and able to discriminate between the pathological and healthy condition. [ABSTRACT FROM AUTHOR]

Due to the large variety of converters' configurations, many different sensorless controllers are available in the literature, each one suited for a particular converter. The need for different configurations, especially on the same power supply, make it clear the advantage of having a shared control algorithm. This paper presents a unified nonlinear robust current observer for buck, boost and buck–boost converters in synchronous and asynchronous configurations. The unified observer speeds up the design, tuning and the implementation, and requires a memory cheaper code, easier to certify. Simulation and experimental results are presented to validate the approach in different scenarios. [ABSTRACT FROM AUTHOR]

Emergency signs represent the easiest way to assist a person during an emergency building evacuation, such as in a fire. However, previous studies show that signs can also not be correctly perceived (because of: smoke/lighting conditions, sign positions in relation to individual's position, pictograms dimensions), consequently becoming useless. This study focuses on the effectiveness of exit signs, in terms of type and position. The related numerical quantification is founded on an objective experimental neurological method. A neurological stimulus (P300) is generated when the individual understands the meaning of the sign. It is so possible to objectively evaluate by using a neuro-headset the performance of each subject in terms of perceptual attention. The use of similar direct data overlooks errors connected to conscious or unconscious individual mediation in answers. Evacuation motion simulations are performed using a virtual reality environment because of limits in the neuro-headset use when walking. Two types of exit signs (reflective and photoluminescent) are tested considering different positions and environmental conditions (illuminated and not). The sign identification distance is retrieved and compared to results of real-world experiments. Finally, the sign effectiveness is also inquired in terms of statistical P300 presence on the analysed sample. [ABSTRACT FROM AUTHOR]

The present paper deals with the defect detection and diagnosis of induction motor, based on motor current signature analysis in a quality control scenario. In order to develop a monitoring system and improve the reliability of induction motors, Clarke–Concordia transformation and kernel density estimation are employed to estimate the probability density function of data related to healthy and faulty motors. Kullback–Leibler divergence identifies the dissimilarity between two probability distributions and it is used as an index for the automatic defects identification. Kernel density estimation is improved by fast Gaussian transform. Since these techniques achieve a remarkable computational cost reduction respect the standard kernel density estimation, the developed monitoring procedure became applicable on line, as a Quality Control method for the end of production line test. Several simulations and experimentations are carried out in order to verify the proposed methodology effectiveness: broken rotor bars and connectors are simulated, while experimentations are carried out on real motors at the end of production line. Results show that the proposed data-driven diagnosis procedure is able to detect and diagnose different induction motor faults and defects, improving the reliability of induction machines in quality control scenario. [ABSTRACT FROM AUTHOR]

In recent years, Italy has seen a rapid growth in the PV (photovoltaic) sector, following the introduction of the FIT (feed in tariff) scheme known as Conto Energia. In July 2013 the Italian government definitively cut FITs, leaving only tax benefits and a revised net metering scheme (known as “Scambio sul Posto”) for new PV installations. In this scenario, the design of a new PV plant ensuring savings on electricity bills is strongly related to household electricity consumption patterns. This paper presents a high-resolution model of domestic electricity use. The model is based on Fuzzy Logic Inference System. Using as inputs patterns of active occupancy and typical domestic habits, the fuzzy model give as output the likelihood to start each appliance within the next minute. The model has been validated with electricity demand data recorded over the period of one year within 12 dwellings in the central east coast of Italy. The tool has been used to evaluate the self consumption percentage to correctly size a residential photovoltaic plant in a case study. A cost benefits analysis is presented to show the effectiveness of PV-generation in the new Italian scenario. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a robust discrete-time sliding mode control coupled with an uncertainty estimator designed for planar robotic manipulators. Experimental evidence shows satisfactory trajectory tracking performances and noticeable robustness in the presence of model inaccuracies, disturbances and payload perturbations. Ultimate boundedness of the tracking errors is proved, as well as boundedness of the estimation error with arbitrary precision. [Copyright &y& Elsevier]