A finite control set model predictive control (FCS-MPC) strategy is investigated for the speed regulation of permanent magnet synchronous motors (PMSMs). A cascaded control structure is implemented, consisting of an inner FCS-MPC current loop, enhanced by a variable structure (VS) approach, and an outer discrete-time VS control (DTVSC) speed loop. The adoption of FCS-MPC for current regulation in industrial PMSMs is driven by its inherent fast dynamic response, ability to enforce constraints, and the potential for modulator-less operation. Furthermore, DTVSC has proven to be an effective PMSM control strategy, particularly valued for its robustness in the face of model inaccuracies and external disturbances. The proposed control methodology, compared to the industry-standard PI cascaded control implemented by Whirlpool in domestic appliances, provides an improvement in control performance and robustness characteristics.

This paper proposes a discrete-time sliding mode control (DTSMC) approach for controlling a permanent-magnet synchronous motor (PMSM). To ensure precise speed tracking, a cascade control strategy is introduced. The stability of the proposed control scheme is analyzed, and a formal proof of the ultimate boundedness of the speed tracking error is provided. The developed control methodology yields superior control performance and robustness characteristics relative to the industry-standard PI cascaded control employed by Whirlpool in domestic appliances. Experimental testing of the control scheme was also conducted using a commercial PMSM drive.

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.

Nowadays many machines and robots are programmed to perform the same task repeatedly. The Iterative Learning Control (ILC) paradigm is based on the idea that the performance of a system that executes the same trial multiple times can be improved by learning from the previous iterations. The objective of ILC is to improve the batch process performance by incorporating past trials error information into the control reference signal for the subsequent iteration. The ILC algorithms are categorized with respect to the number of past iterations considered to compute the next control signal and the first order ILC includes those algorithms considering only information about the last trial. In this paper different first order ILC update laws have been considered and compared controlling a Single-Input Single-Output (SISO) micro-positioning piezostage system. The proposed comparison allows to evaluate the performance of different first order ILC algorithms tested on the considered real world case study.

This paper presents a Model Predictive Control (MPC) strategy for a triaxial piezoelectric actuators (PAs) system. PAs systems require appropriate controllers to guarantee fast and high-precision positioning performances avoiding effects of non-linearities. Typically, commercial systems provide integrated Proportional-Integral (PI) controllers guarantying to maintain system stability in the presence of uncertainty and disturbance. MPC owes its success to the ability of optimally regulate multivariable systems through the minimization of a Quadratic Programming (QP) problem subjected to prescribed constraints. Alternatively, unconstrained MPC eliminates constrains from the problem, reducing the number of operations elapsed to compute the solution. The aim of this work is to design an unconstrained MPC for a 3-DOF PA replacing PI controllers to improve control performances by a smaller increase of required computational effort. The system is described by a Multi-Input Multi-Output (MIMO) Linear Time-Invariant (LTI) model, experimentally identified by the open-loop real plant response. Effectiveness of the proposed method is validated by simulation tests and experiments on the real system, comparing MPC with PI controllers tuned to guarantee common PA stability requirements.

The paper focuses on variable-rotor-speed/variable-blade-pitch wind turbines operating in the region of high wind speeds, where control is aimed at limiting the turbine energy capture to the rated power value. A robust sliding mode approach is proposed, using the blade pitch as control input, in order to regulate the rotor speed to a fixed rated value, in the presence of uncertainties characterizing the wind turbine model. Closed loop convergence of the overall control system is proved. The proposed control solution has been validated on a 5-MW three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. A comparison with the standard FAST baseline controller [1], [2] has been also included.

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.

Piezoelectric drivers are widely used in nanoscale image acquisition systems. A source of performance degradation of these drivers is hysteresis, which introduces low rate noise and causes a slow continuous drift, decreasing the quality of scanned images. In this paper a sliding mode control policy, based on an estimator of the perturbation due to hysteresis, is applied to the control of a piezostage in a three dimensional images acquisition system, in order to improve control performances and final scanning results. The presented solution has been experimentally tested and compared with the Proportional-Integrative-Derivative (PID) controllers provided with the piezoelectric acquisition system, showing a noticeable improvement both in the piezostage behavior and in the images quality.

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.

This paper focuses on the so-called sensorless speed-tracking control for permanent magnet synchronous motors. A control strategy is proposed allowing to extract rotor position using electrical signals and an observer of electrical variables, and to guarantee the robust asymptotical tracking of a reference speed without measuring rotor velocity and position. Bounded parameter variations are supposed to affect the mechanical system, and a (possibly time-varying) uncertain load torque is considered. Reported experimental evidence shows the effectiveness of the proposed DSP-based control policy.

In this paper the problem of icing detection is considered for wind turbines operating in medium speed wind region (Region 2) and subject to a control law tracking the Maximum Delivery Point of the power coefficient characteristic. Based on an observer of the rotor angular acceleration, rotor inertia is estimated in order to detect its eventual increase due to icing. Moreover, the observed value of rotor inertia can be potentially used for updating the controller parameters or to stop the turbine when icing is too severe. The proposed approach has been tested by intensive MatLab®simulations using the NREL 5 MW wind turbine model.

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.

The control of DC-DC converters for high performance applications usually relies on Current Mode Control (CMC). Compared to voltage mode control, it guarantees automatic over-current protection, and a better closed loop stability, together with improved transient response. This paper presents a Model Predictive Control (MPC) algorithm for the regulation of the voltage reference signal in CMC. The control of pre-compensated systems via MPC is common in several fields, such as automotive and aerospace, and power electronics is a perfect candidate to exploit this hierarchical structure as well. Indeed, controllers for power converters are often coded in the integrated circuits, and cannot be changed. Furthermore the possible multirate structure allows to exploit the performance of MPC less affecting the computational cost. The paper describes the design of an MPC regulator for a synchronous buck converter, when a primal CMC is already coded. Performance improvements of the proposed controller are reported.

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 this paper the control of a boost converter in the presence of an uncertain load resistance is addressed. Exploiting a suitable change of coordinates and pointing out the internal and the external dynamics of the converter, a sliding mode control law is considered for the regulation of the converter output voltage. A sliding mode observer is introduced, whose dynamics allows to estimate the unknown load resistance. The robust control algorithm is designed to actuate directly the switching device, thus eliminating the need for a waveform modulator. The overall control system has been tested in simulated scenario. The performances are evaluated comparing with a standard double loop control scheme for boost converter, with a linear controller in the external loop and a sliding mode controller in the internal loop. Simulation tests show the effectiveness of the proposed approach, which is superior to the standard controller.

In recent years the European Union and, moreover, Italy has seen a rapid growth in the photovoltaic (PV) sector, following the introduction of the feed in tariff (FIT) 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 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 focus of this work is the use of this novel fuzzy model to correctly size a residential photovoltaic plant and evaluate the economic benefits of energy management actions in a case study. A cost benefits analysis is presented to quantify its effectiveness in the new net metering Italian scenario.

In this paper a Power Factor Control (PFC) of an AC-DC boost converter operating in light load condition has been presented. A Passivity-Based current control able to operate in either Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM) has been presented. Cascaded control with outer PI voltage loop and intermediary input voltage feedforward has been implemented in order to increase Power Factor (PF). The proposed solution has been numerically tested using a powerful software simulation platform.

This paper presents a high-resolution model of domestic electricity use, based on Fuzzy Logic Inference System (FIS). The model is built with a “bottom-up” approach and the basic block is the single appliance. Using as inputs patterns of active occupancy (i.e. when people are at home and awake) and typical domestic habits (i.e. start frequency of some appliances), the FIS model give as output the starting probability of each appliance. A post processor enable the appliances start in order to create a one-min resolution electricity demand data. In order to validate the model, electricity demand was recorded over the period of one year within 12 dwellings in the central east coast of Italy. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics.

In this paper the prototype of a novel photovoltaic and water heating system (PVT) is designed. A dynamic model of the collector based on the energy transfer phenomenon has been derived. Experimental tests on the prototype of the PVT collector have been used to validate the model under the effect of water mass flow rate and to show the performance improvement with respect a traditional photovoltaic panel. In the end a Fuzzy control scheme is proposed to maximize the performances of the collector and the results have been compared with a traditional PID controller.

In this paper a speed and current control for Permanent Magnet Synchronous Motors (PMSMs) with incremental encoder sensor has been designed. A Model Predictive Control (MPC) technique for PMSM has been explored, considering an off-line explicit MPC solution resulting in a PieceWise Affine (PWA) controller formulation. Reported numerical results show that the proposed controller enhances the overall performances of common cascaded Field Oriented Control (FOC) in different scenarios. The proposed solution has been experimentally tested on a commercial PMSM equipped with a control system based on Digital Signal Processor (DSP).

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.

This paper focuses on a robust power generation control strategy for a variable speed wind energy conversion system. The proposed control strategy, by a robust observer of the aerodynamic torque, produces the required generator electromagnetic torque. The robust ultimate boundedness of the observation error and the tracking error, with arbitrary precision, is proved. The proposed sliding mode control approach has been validated on a 1.5-MW three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Reported numerical simulations show that the proposed control solution is effective in terms of optimal power extraction and it is robust with respect to disturbances affecting the system.

This paper presents a discrete-time sliding mode control based on neural networks designed for robotic manipulators. Radial basis function neural networks are used to learn about uncertainties affecting the system. The online learning algorithm combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks. A method to improve the run-time performance for the real-time implementation of the learning algorithm has been considered. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies, disturbances and payload perturbations.

This paper presents a discrete-time sliding mode control based on prediction compensation of uncertainties for planar robotic manipulators. Autoregressive models, identified on-line by Kalman Filters, are used to learn about uncertainties affecting the system. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies.

This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process cannot be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and the stability of this multi-controller system is analyzed using the Lyapunov theory. The performance of the switched controller is evaluated by numerical simulations

In this paper, we present a set of robust and efficient algorithms with O(N) cost for the following situations: object detection with a laser ranger; mobile robot pose estimation and a FastSLAM improved implementation. Objected detection is mainly based on a novel multiple line fitting method, related with walls at the environment. This method assumes that walls at the environment constitute a regular constrained angles. A line-based pose estimation method is also proposed, based on least-squares (LS). This method performs the matching of detected lines and estimated map lines and it can provide the global pose estimation under assumption of known data-association. FastSLAM 1.0 has been improved by considering the estimated pose with the LS-approach to re-allocate each particle of the posterior distribution. This approach has a lower computational cost than EKF approach in FastSLAM 2.0. The three algorithms have been combined in order to perform an efficient self-localization and map building process, tested for indoor environments with real data. And results show that the ideas proposed in this paper could aim in closing the loop and also to improve the overall estimation performance

In this paper the localization and environment feature estimation problems are formulated in a stochastic setting and an Extended Kalman Filtering (EKF) approach is proposed for the integration of odometric, gyroscope and sonar measures. As gyroscopic measures are much more reliable than the other ones, the localization algorithm gives rise to a nearly singular EKF. This problem is dealt with defining a lower order non singular EKF.