Matteo Pirro

Solid Waste Management Architecture Using Wireless Sensor Network Technology

In many application fields such as home, industry, environment and health, different Wireless Sensor Network (WSN) applications have been developed to solve management problems with smart implementations. This approach can be applied in the filed of solid waste management. In this paper a new architecture is proposed with the aim to improve the on-site handling and transfer optimization in the waste management process. The system architecture is based on sensor nodes and makes use of Data Transfer Nodes (DTN) in order to provide to a remote server the retrieved data measurements from the garbage bins filling. A remote monitoring solution has been implemented, providing user the possibility to interact with the system by using a web browser. Several activities with the aim to provide a Decision Support System (DSS) able to find solutions for resources organization problems linked to solid waste management have been started.

A smart lighting system for industrial and domestic use

The goal of this work is to develop a smart LED lighting system for industrial and domestic use, taking into account visual comfort and energy saving of interior lighting. The idea is to control the lighting level in an energy efficient way, keeping a desired light level where it is needed, while regulating it to a minimum where not required. In order to achieve this goal, a single control unit is needed for each lamp. In this way the system can individually control the desired light level, adapting the LED illumination according to the environment in which it is installed, by means of light sensors, motion sensors and a smart control system. Experimental results are provided to show the effectiveness of the proposed solution.

Design of a Home Energy Management System by Online Neural Networks

Abstract In this paper the design and test of a home energy management system have been considered. The device, monitoring home loads, detecting and forecasting photovoltaic (PV) power production and home consumptions, informs and influence users behaviour on their energy demand. A neural network based self-learning prediction algorithm is used to forecast the power production of the PV plant and the household consumptions over a determined time horizon. A semi auto active demand side management technique is used to maximize the amount of PV electricity directly used on-site. The proposed solution has been experimentally tested in 3 houses with 3.3 KWp PV plant.

Passivity-Based PFC for Interleaved Boost Converter of PMSM Drives

Abstract In this paper a sensorless passivity-based control with input voltage feedforward has been designed for a boost AC-DC interleaved converter to increase the Power Factor (PF) in Permanent Magnet Synchronous Motor (PMSM) drives. A robust current observer has been adopted. The proposed solution has been experimentally tested on a commercial PMSM drive equipped with a control system based on a microcontroller (MCU) of the Texas Instrument (TI) family C 2000 ™.

Current sensorless solution for PFC boost converter operating both in DCM and CCM

In this paper a current sensorless solution for Power Factor Control (PFC) of an AC-DC boost converter operating in light load condition has been presented. A PI-based control with input voltage feedforward has been implemented in order to increase Power Factor (PF). A current observer, able to operate in either Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM), has been adopted in order to eliminate the need for expensive current sensors and to improve boost converter performances under light load conditions. The proposed solution has been numerically tested in three different load conditions using a powerful software simulation platform.

Indoor thermal comfort control through fuzzy logic PMV optimization

Control and monitoring of indoor thermal conditions represent crucial tasks for peoples 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.

A Smart Lighting System for Visual Comfort and Energy Savings in Industrial and Domestic Use

Abstract The goal of this work is to develop a smart light-emitting diode lighting system for industrial and domestic use with several advantages over conventional systems, namely energy saving, high reliability, and visual comfort of interior lighting. This is achieved by integrating a smart control module and a fault diagnosis and prognosis module within a conventional lighting system. The first module controls the lighting level in an energy-efficient way, keeping a desired light level where it is needed while regulating it to a minimum where not required; this is achieved by fully exploiting fuzzy logic and proportional-integrative-derivative controllers. The second module performs fault diagnosis on the light-emitting diode system and predicts when light-emitting diode maintenance should be performed; this is achieved by employing both hardware redundancy and signal-based approaches. Interaction between the two modules permits maintenance of a desired level of light even in the case of failures on one or more light-emitting diodes. The overall system has been experimentally validated in three different scenarios.

Explicit sensorless model predictive control of synchronous buck converter

In this paper a Model Predictive Control (MPC) for buck synchronous DC-DC converter has been designed. The proposed solution considers an off-line explicit MPC formulation resulting in a PieceWise Affine (PWA) controller structure. A model based observer for inductor current has been taken into account to overcome the several problems related to sensors in high current ripple DC converters. Reported numerical results show that the proposed control scheme enhances the overall performances of common averaged current mode control with linear regulators.

Model predictive control solution for Permanent Magnet Synchronous Motors

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).

Synchronous buck converter control via robust periodic pole assignment

In this paper a robust eigenvalue assignement technique has been adopted to control a synchronous buck DC-DC converter; the proposed solution consists in a digital periodic state feedback control. A time-invariant state feedback control law has been designed to evaluate the performance of the periodic solution. The proposed algorithms have been numerically tested with a powerful simulation platform: numerical results show that the periodic control scheme enhances the overall performances of common state feedback control in terms of robustness against parameter variations.