Alessandro Baldini

Room occupancy detection: Combining RSS analysis and fuzzy logic

In this paper we focus our attention on the world of Internet of Things (IoT) objects and their potential for human indoor localization. Our aim is to investigate how Received Signal Strength (RSS) can be effectively used for identifying the position of a person at home, by exploiting common IoT communication networks. We propose a plug and play solution where the Anchor Nodes (ANs) are represented by smart objects located in the house, while the Unknown Node (UN) can be any smart object held by the user. The proposed solution automatically identifies the rooms where the smart objects are placed, by comparing a fuzzy weighted distance matrix derived from the anchor signals, with a threshold weighted distance matrix derived from the distances between rooms. The information can be easily integrated in any IoT environment to provide the estimation of the user position, without requiring the a priori knowledge of the positions of the anchor nodes.

A novel RSSI based approach for human indoor localization: The Fuzzy Discrete Multilateration

In this paper a new algorithm for indoor localization, namely Fuzzy Discrete Multilateration (FDM), is proposed. As the name suggests, it elaborates data from any number of transmitters (anchor nodes), and returns the estimated position of an unknown receiver. Furthermore, two cascade fuzzy inference systems are employed to evaluate the reliability of the data gathered from each beacon. The algorithm has been tested in different real world environments, where the anchor nodes are smart objects and the unknown node is any smart object held by the user to be localized. The performances of our algorithm has been compared with those of three well known localization algorithms (with a beacon density ranging from 0.03 to 0.1 beacon/m2) and results are shown.

Dynamic surface fault tolerant control for underwater remotely operated vehicles

In this paper, we present a two stages actuator Fault Tolerant Control (FTC) strategy for the trajectory tracking of a Remotely Operated Vehicle (ROV). Dynamic Surface Control (DSC) is used to generate the moment and forces required by the vehicle to perform the desired motion. In the second stage of the control system, a fault tolerant thruster allocation policy is employed to distribute moment and forces among the thrusters. Exhaustive simulations have been carried out in order to compare the performance of the proposed solution with respect to different control techniques (i.e., PID, backstepping and sliding mode approaches). Saturations, actuator dynamics, sensor noises and time discretization are considered, in fault-free and faulty conditions. Furthermore, in order to provide a fair and exhaustive comparison of the control techniques, the same meta-heuristic approach, namely Artificial Bee Colony algorithm (ABC), has been employed to tune the controllers parameters.

Nonlinear control of a photovoltaic battery system via ABC-tuned Dynamic Surface Controller

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.

Particle Swarm Optimization Based Sliding Mode Control Design: Application to a Quadrotor Vehicle

In this chapter, a design method for determining the optimal sliding mode controller parameters for a quadrotor dynamic model using the Particle Swarm Optimization algorithm is presented. In particular, due to the effort to determine optimal or near optimal sliding mode parameters, which depend on the nature of the considered dynamic model, a population based solution is proposed to tune the parameters. The proposed population based-method tunes the controller parameters (boundary layers and gains) according to a fitness function that measures the controller performances. A comparison of the designed sliding mode control with two popular controllers (PID and Backstepping) applied to a quadrotor dynamic model is proposed. In particular sliding mode control shows better performances in terms of steady state and transient response, as confirmed by performance indexes IAE, ISE, ITAE and ITSE.

Robust Control of a Photovoltaic Battery System via Fuzzy Sliding Mode Approach

In this chapter we propose a novel fuzzy sliding mode approach 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. Sliding Mode Control (SMC) is a well-known approach to control systems under heavy uncertain conditions. However, one of the major drawbacks of this control technique is the high frequency chattering generated by the switching control term. In the proposed solution, we introduce a fuzzy inference system to set the controller parameters (boundary layer and gains) according to the measured irradiance. A comparison of the designed Fuzzy Sliding Mode Control (FSMC) with two popular controllers (PI and Backstepping) is performed. In particular, FSMC shows better performances in terms of steady state chattering and transient response, as confirmed by IAE, ISE and ITAE performance indexes.

Active Fault Tolerant Control of Remotely Operated Vehicles via Control Effort Redistribution

An active fault tolerant control technique for Underwater Remotely Operated Vehicles is proposed in this paper. The main objective is to develop a controller for the tracking problem, which is robust against possible actuator faults and failures. The main advantage of the proposed fault tolerant control scheme is to develop a unique controller, and thus a unique set of control parameters, regardless the presence of faults and failures. This is achieved through a redistribution of the control effort on the healthy actuators. Simulation results are provided to demonstrate the viability of the proposed fault accommodating technique.Copyright