This paper presents the preliminary results of a Linear Parameter Varying-Autoregressive eXogenous model, identified through Least Squares Support Vector Machines, able to optimally drive a robotic arm system by emulating the performance of a Nonlinear Model Predictive Control (NMPC) policy. The support vector machine framework is employed to replicate the control performance of a computationally demanding NMPC. Due to the nonlinear characteristics of the robotic arm, the NMPC is suitable to guarantee expected control performance. However, its application in real-time systems with fast dynamics is limited by high memory and computational demands required at each sampling instant. In this work, the linear parameter varying model is trained using a data-driven approach to imitate the control actions of the NMPC across different scenarios. The proposed controller and the original NMPC are evaluated in simulation, considering multiple operating conditions of the robotic arm. The control performance of both approaches is then compared to assess the effectiveness of the proposed method.

A geofence is a virtual perimeter representing the limits of a real-world operating area. The development of a control policy allowing to guarantee the safety of the Unmanned Aircraft Vehicles' (UAVs) users and stakeholders represents an important industrial world problem, yet studied in depth by the international scientific research community. In this paper, a geofencing system for UAVs based on the Model Predictive Control (MPC) paradigm is proposed. MPC permits to optimally drive dynamical systems explicitly imposing constraints on input and output by the prediction of the future evolution of the controlled plant. In this paper, an MPC policy is proposed to impose the geofencing area of a pre-compensated multi-rotor UAV. The proposed approach considers recomputing iteratively the controlled UAV speed constraints with respect to a prescribed maximum vehicle deceleration, in order to correctly impose the limits on vehicle speed and to stop the UAV on the borders of the prescribed geofence operating area. The proposed algorithm has been verified in simulation tests controlling a pre-compensated multi-rotor vehicle in a considered control scenario.

This paper presents a preliminary study considering the design of an adaptive torque allocation policy for electric vehicles combining optimal control with data-driven techniques. The vehicle is equipped with four independent actuated wheels driven by electric motors. The policy aims to control the vehicle powertrain by allocating available power among motors to satisfy the driver control torque request and adjust torque allocated to different motors according to estimated wheels slip ratio change due to terrain varying conditions. A constrained optimal torque allocation algorithm is designed to distribute available power among wheels. In order to adjust the power allocation result, a data-driven adaptive policy is designed to adjust the control allocation parameters and the torque distribution reflecting wheel's operating conditions. The combination of torque allocation and data-driven adaptation policies permits the adjustment of the allocated power according to the wheel/road contact conditions. The algorithm has been tested and validated in simulation, showing the improvement given by the proposed approach compared with respect to the control system neglecting the data-driven adaptation of the torque allocation policy.

This article presents a Fault Detection (FD) method to deal with propeller faults on multirotor drones in real-time. Several solutions have been proposed in the literature, however, they depend on additional sensors and/or dedicated hardware to deal with heavy computational complexity. So, they cannot be implemented in off-the-shelf commercial devices, i.e., without the aid of additional on-board sensors and/or extra computational power. The proposed method, instead, requires the on-board Inertial Measurement Unit (IMU) data only: by combining Finite Impulse Response (FIR), together with sparse classifiers, only a subset of the features is actually needed online and the FD is thus feasible in real-time. Design and tests are based on real flight data from a hexarotor, equipped with a conventional ArduPilot-based controller. The classification accuracy in testing is up to 93.37% (98.21%) with a binary tree (Linear Support Vector Machine (LSVM)). Moreover, the space and time complexity of the proposed method is low: on a PixHawk Cube flight controller, it requires less than 2% of the cycle time, and can then run in real-time. Finally, the proposed fault detection solution is model-free and it can be easily generalized to other multirotor vehicles.

This article presents a fault diagnosis algorithm for rotating machinery based on the Wasserstein distance. Recently, the Wasserstein distance has been proposed as a new research direction to find better distribution mapping when compared with other popular statistical distances and divergences. In this work, first, frequency- and time-based features are extracted by vibration signals, and second, the Wasserstein distance is considered for the learning phase to discriminate the different machine operating conditions. Specifically, the 1-D Wasserstein distance is considered due to its low computational burden because it can be evaluated directly by the order statistics of the extracted features. Furthermore, a distance weighting stage based on neighborhood component features selection (NCFS) is exploited to achieve robust fault diagnosis at low signal-to-noise ratio (SNR) conditions and with high-dimensional features. In detail, the NCFS framework is here adapted to weight 1-D Wasserstein distances evaluated from time/frequency features. Experiments are conducted on two benchmark data sets to verify the effectiveness of the proposed fault diagnosis method at different SNR conditions. The comparison with state-of-the-art fault diagnosis algorithms shows promising results.Note to Practitioners—This article was motivated by the problem of fault diagnosis of rotating machinery under low SNR and different machine operating conditions. The algorithm employs a statistical distance-based fault diagnosis technique, which permits to obtain an estimation of the fault signature without the need for training a classifier. The algorithm is computationally efficient during the training and testing stages, and thus, it can be used in embedded hardware. Finally, the proposed methodology can be applied to other application domains such as system monitoring and prognostics, which can help to schedule the maintenance of rotating machinery.

Recurrence quantification analysis (RQA) allows to quantify the periodic behavior using recurrence plots instead of deriving information purely from visual analysis. The current study presents a preliminary analysis of stator-current measurements for electric motor fault detection and classification by means of the recurrence quantification theory. Firstly, a preliminary visual inspection of the recurrence plots of stator-current measurements for healthy and faulty electric motors is presented. Thereafter, the following RQ metrics are analyzed: the recurrence rate, the determinism, the divergence, the Shannon entropy, the laminarity and the trapping time. Then, the RQ metrics are used as predictors for fault detection and classification. The classification results (100% fault classification accuracy), which are presented using the linear support vector machine classifier, show that the RQA can be considered as a tool for motor current signature analysis.

Here, the authors present a low cost telerehabilitation system made up of a commercial red–green–blue depth (RGB‐D) camera and a web‐based platform. The authors goal is to monitor and assess subject movement providing acceptable and usable at‐home remote rehabilitation services without the presence of a clinician. Clinical goals, defined by physiotherapists, are firstly translated into motion analysis features. A Takagi Sugeno fuzzy inference system (FIS) is then proposed to evaluate and combine these features into scores. In this stage, the ‘collaborative design’ paradigm is used in depth and complete manner: the contribution of the clinician is not limited only to the rules definition but enters in the core of the evaluation algorithm through the definition of the fuzzy rules. A case study on low back pain rehabilitation involving 40 subjects, 5 exercises, and 4 physiotherapists is then presented to the effectiveness of the proposed system. Results of the validation of the system aimed at the assessment of the reliability of the proposed approach show high correlations between clinician evaluation and FIS scores. In this scenario, due to the high correlation, each FIS could represent a virtual alter‐ego of the physiotherapist which enable a real time and free second opinion.

This paper proposes a control methodology based on Dynamic Surface Control (DSC) to manage the power flow of a photovoltaic (PV) battery system. In particular, due to the inner stochastic nature and intermittency of the solar production and in order to face the irradiance rapid changes, a robust and fast controller is needed. Dynamic Surface Control is a modified version of Backstepping control that avoids the explosion of terms, which is a typical drawback of the Backstepping control and furthermore it is not affected by the well known problem of chattering, which affects Sliding Mode controllers. Dynamic Surface Control is compared to the conventional Proportional-Integral-Derivative controller (PID). In particular, DSC shows better performances in terms of steady state chattering and transient response, as confirmed by the Integral of the Absolute value of Error (IAE), Integral of the Squared Error (ISE) and Integral of Time multiplied by the Absolute value of Error (ITAE) performance indexes.

In this paper, a new open-source SW framework for energy management is presented. Its name is rEMpy, which stands for residential Energy Management in python. The framework has a modular structure and it is composed by an optimal scheduler, a user interface, a prediction module and the building thermal model. Unlike most of the EMs in literature, rEMpy is open-source, can be fully customized (in terms of tasks, modules and algorithms) and integrates in real-time a thermal modelling software. In this contribution, an overview of the rEMpy and its constitutive parts is given first, followed by a detailed description of the rEMpy modules and the communication system. The Computational Intelligence algorithms which perform forecasting, thermal modelling and optimal scheduling are also presented. The performance of rEMpy is finally evaluated in two case studies with different heating technologies and the results are reported and discussed.

In this paper we present a swarm intelligence approach to tackle the optimal sizing problem of all the microgrid (MG) components. A model has been built for a grid-connected MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of energy savings benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using artificial bee colonies (ABC) algorithm. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.

In recent years several statistical methods have been applied to condition monitoring of various processes under linearity and stationarity assumptions. However most of the actual industrial processes, e.g. in the chemical sector, are strongly nonlinear. Furthermore the hypothesis of data Gaussian distribution does not often hold, thus causing a decrease of the fault detection accuracy. In this paper a Switching Linear Regression (SLR) approach is firstly proposed in a fault detection scenario. The basic idea is to estimate different Linear Regression models through an arbitrary clustering algorithm and then switching among these models. The developed algorithm allows to deal with nonlinear processes. The proposed fault detection approach is applied to two simulated test bench and on the Tennessee Eastman process benchmark. Furthermore, compared with the Linear Regression algorithm, SLR shows better performance in terms of fault detection accuracy.

In this paper we present a computational intelligence approach to solve the optimal sizing problem of grid connected microgrid (MG) components. A simulation model has been built for the MG and comprises households, solar photovoltaic (PV) plants, wind turbines (WT) and energy storage (ES) systems. The goal is the maximization of the long term economic benefits for the community being served by the MG. We choose to optimize the net present value (NPV) of the whole investment in a cost benefits analysis (CBA) scenario. In particular, due to the complexity and the high-dimensionality of the problem, we solved it using population based optimization techniques. We tested four different algorithms in their basic form, i.e. artificial bee colonies, particle swarm optimization, genetic algorithm and gravitational search algorithm, comparing their performances. The effectiveness of the approach is tested in a case study where the optimal ratings for the PVs, WTs and ESs are determined using real weather and electrical demand data in the central east part of Italy.

In the modern industrial sector there is a growing interest on electric motors safety, reliability and maintainability. In this context health monitoring and fault detection are crucial tasks to be performed. In this paper we introduce a univariate filter method based on Bayes error for feature selection in a fault detection scenario. The feature selection algorithm firstly estimates the probability density function of the data. At a second stage we compute the PDFs intersection area which is related to the Bayes error. Finally we choose the features with the minimum Bayes error. In order to properly test the proposed algorithm, a starter motor assembly line has been considered as a case study. Features extraction is performed on statistical time and frequency domain analysis while the quadratic classifier is used for the final fault detection. Performances of the proposed approach have been compared with those of Relieff and SFS algorithms on a 649 motors data set. Results show that our method outperforms the other two in terms of Area Under Curve - Receiver Operating Characteristic (AUC-ROC).

Control and monitoring of indoor thermal conditions represent crucial tasks for people's satisfaction in working and living spaces. Among all standards released, predicted mean vote (PMV) is the international index adopted to define users thermal comfort conditions in thermal moderate environments. PMV is a nonlinear function of various quantities, which generally limits its applicability to the heating, ventilation, and air conditioning (HVAC) control problem. Furthermore this index does not consider explicitly outdoor weather conditions. In order to overcome both problems, we introduce a novel fuzzy controller for HVAC systems. The control, considering PMV index value as well as outdoor weather conditions, has been experimentally tested in a working space in the central east coast of Italy. Furthermore temperature regulation performances have been compared with those of a classical PID.

Early detection of abnormalities for electrical motors is a key point to reduce economic losses caused by unscheduled maintenance and shutdown time. In this context, health monitoring and fault diagnosis are crucial tasks to be performed. We introduce a novel Linear Discriminant Analysis (LDA) based algorithm to deal with fault data dimension reduction and fault detection issues. In particular the algorithm, namely Δ-LDA, is designed to overcome the problem of a between-class scatter matrix trace very close to zero. Indeed, if the information of the expected value is not sufficient to discriminate the classes, we propose the use of the difference of covariance matrices. A performance comparison with other conventional methods, e.g. principal component analysis and classical LDA, is proposed. In particular experimental results show that the proposed algorithm improves the classification accuracy if the classes are overlapped, and gives comparable results in the remaining scenarios.

In the contest of household energy management, a growing interest is addressed to smart system development, able to monitor and manage resources in order to minimize wasting. One of the key factors in curbing energy consumption in the household sector is the amendment of occupant erroneous behaviours and systems malfunctioning, due to the lack of awareness of the final user. Indeed the benefits achievable with energy efficiency could be either amplified or neutralized by, respectively, good or bad practices carried out by the final users. Authors propose a diagnostic system for home energy management application able to detect faults and occupant behaviours. In particular a nonlinear monitoring method, based on Kernel Canonical Variate Analysis, is developed. To remove the assumption of normality, Upper Control Limits are derived from the estimated Probability Density Function through Kernel Density Estimation. The proposed method is applied to smart home temperature sensors to detect anomalies respect to efficient user behaviours and sensors and actuators faults. The method is tested on experimental data acquired in a real apartment.

The present paper proposes a novel data-driven Fault Detection and Diagnosis algorithm for induction motors based on Motor Current Signature Analysis. Principal Component Analysis is used to reduce the three-phase currents space in two dimensions. Then, Kernel Density Estimation is adopted to estimate the Probability Density Function of healthy and of each faulty motors, which will give typical patterns that can be used to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two determined probability distributions, that allows the automatic identification of distinct fault types. Several simulations and experimental results are carried out using two benchmarks in order to verify the effectiveness of the proposed methodology: the first is used to prove appropriateness of the method for air gap eccentricity fault diagnosis and the second is used to prove suitability of the method for rotor broken bars and connectors fault diagnosis. Simulations and classification results prove that the proposed Fault Detection and Diagnosis procedure is able to detect and diagnose different induction motor fault types.

The present paper proposes an auditory BCI paradigm for systems based on P300 signals which are generated by auditory stimuli characterized by different sound typologies and locations. A Head Related Transfer Function approach is adopted to virtualize auditory stimuli. When virtualized audio is used, the user has to focus the attention both on the type and location of the stimulus, thus generating P300 signals whose amplitude is higher than that generated without audio virtualization. Classification is performed by Support Vector Machines in which gaussian radial basis functions are used as kernel functions. The system has been validated with 14 users, who were asked to choose one among five common spoken words, previously virtualized and transmitted to stereophonic headphones. Classification results prove that the proposed auditory BCI system performed similarly to common visual BCI P300 systems, representing then an alternative to visual BCI for users with visual impairments.

In paper mill plants, the competition for increasing efficiency and reducing costs is a primary purpose. Fault detection and diagnosis can help by minimize the loss of production. In particular for the stock preparation sub-process a signal based fault detection and isolation procedure is developed. Multi-Scale Principal Component Analysis (MSPCA) is used to monitor some critical variables of the stock preparation of a paper mill plant in order to diagnose faults and malfunctions. MSPCA simultaneously extracts both, cross correlation across the sensors (PCA approach) and auto-correlation within a sensor (Wavelet approach). The advantage of MSPCA is validated on considered paper mill plant where several sensors are installed to control and monitor the automation system.