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In this paper we consider the problem of state estimation for linear discrete-time Gaussian systems with intermittent observations resulting from packet dropouts. We assume that the receiver does not know the sequence of packet dropouts. This is a typical situation, e.g., in wireless sensor networks. Under this hypothesis, the problem of state estimation has been previously solved by the authors using a detection-estimation approach consisting of two stages: the first is a nonlinear optimal detector, which decides if a packet dropout has occurred, and the second is a time-varying Kalman filter, which is fed with both the observations and the decisions from the first stage. This work extends that solution, introducing a refinement stage whose purpose is to significantly improve the decision on packet dropouts and, in turn, on state estimation. The overall estimator has finite memory and the tradeoff between performance and computational complexity can be easily controlled. Numerical results highlight the effectiveness of the approach based on detection-estimation with refinement, which outperforms both the estimator without refinement and the optimal linear filter of Nahi.

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.

Systems enabling long-term monitoring of physiological data and everyday activities has been the subject of considerable research efforts in the last years, in order to improve the quality of life of patients, elderly people and common citizens at home, out of the hospitalization. With the availability of inexpensive, low power, wireless and integrated devices, current smart homes are typically equipped with a large amount of sensors, which collaboratively process and make deductions from the acquired data on the state of the home, as well as the activities and behaviors of its residents. According to the field of application and the end-users involved (healthy people, elderly, people with disabilities), the definition of the parameters (e.g. heart rate, blood pressure, activity, body mass, etc.) and the appropriate sensors (electrocardiogram, sphygmomanometer, glucometer, etc.) for their acquisition assume a fundamental role. One of the goals of the Italian project Health@Home is to create a network of health sensors and home automation devices to monitor the user's status within the home environment. We present a candidate implementation of such a system, describing the software architecture and the selected components, and a testbed of the architecture, realized in a lab room and used for a preliminary experimental study involving seven users.

The present paper presents a fault tolerant control for dual arm manipulation systems affected by joint faults. The fault is modelled like a reduction of the maximum joint velocity due to partial torque loss of a servomotor. The system is made locally fault tolerant by exploiting its redundancy degrees via two different methods. The first method permits to reduce the manipulators' manipulability loss in case of fault occurrence, by imposing an optimal fault tolerant configuration to both manipulators; this is directly obtained from the knowledge of the Jacobian null space. The second method permits to compensate the loss of the end effectors motion using the saturation null space approach. In order to handle the dual arm system as a unique equivalent manipulator, both methods are formulated according to the relative Jacobian method. The efficiency of the proposed joint fault tolerant control is shown in a case study, where joint faults occur at different time instants.

This paper extends our previous work [1] on the design of a thruster failure tolerant control scheme for underwater vehicles. The proposed control scheme is based on the use of a suitable thruster allocation algorithm, which consists on a modified version of the Moore-Penrose pseudo inverse. In this work, each thruster of the underwater vehicle can rotate, offering a significant advantage to optimize its control. When a thruster experiences a failure, the resulting thrust force, which should be allocated to the failed actuator, is reallocated to the still faultless thrusters. Moreover, the angle of each thrusters is set to minimize the control effort. A bank of controllers is built so that each controller is designed to control the considered underwater vehicle under a specific actuator failure scenario.

Bicycle-sharing is an exciting new model of public-private transport provision that has quickly emerged in the past five years, and represents a possible way for enhancing sustainable mobility in smart cities of the future. This paper proposes a wireless sensor network based management system for electric bicycle-sharing of new design, where each bicycle and docking station is equipped with a measurement and communication device (i.e., a network mote), and is both sensor and actor of the wireless network. Each bicycle can keep track of its position, battery level, and communicate with the docking station, which manages the recharge cycle, and locks or releases the bicycle to the user. Since all the information is shared through the network, the management system can provide high level software services as well (e.g., the booking service). This paper describes in detail the design and the implementation of the proposed wireless sensor network based management system for electric bicycle-sharing.

The integration of Wireless Sensors Networks (WSNs) into Cyber Physical Systems (CPSs) is an important research problem to solve in order to increase the performances, safety, reliability and usability of wireless automation systems. Due to the complexity of real CPSs, emulators and simulators are often used to replace the real control devices and physical connections during the development stage. The most widespread simulators are free, open source, expandable, flexible and fully integrated into mathematical modeling tools; however, the connection at a physical level and the direct interaction with the real process via the WSN are only marginally tackled; moreover, the simulated wireless sensor motes are not able to generate the analogue output typically required for control purposes. A new simulation module for the control of a wireless cyber-physical system is proposed in this paper. The module integrates the COntiki OS JAva Simulator (COOJA), a cross-level wireless sensor network simulator, and the LabVIEW system design software from National Instruments. The proposed software module has been called “GILOO” (Graphical Integration of Labview and cOOja). It allows one to develop and to debug control strategies over the WSN both using virtual or real hardware modules, such as the National Instruments Real-Time Module platform, the CompactRio, the Supervisory Control And Data Acquisition (SCADA), etc. To test the proposed solution, we decided to integrate it with one of the most popular simulators, i.e., the Contiki OS, and wireless motes, i.e., the Sky mote. As a further contribution, the Contiki Sky DAC driver and a new “Advanced Sky GUI” have been proposed and tested in the COOJA Simulator in order to provide the possibility to develop control over the WSN. To test the performances of the proposed GILOO software module, several experimental tests have been made, and interesting preliminary results are reported. The GILOO module has been applied to a smart home mock-up where a networked control has been developed for the LED lighting system.

This paper addresses the problem of sensor fault detection for a wide class of Unmanned Vehicles (UVs). First a general model for UVs, based on the dynamics of a 6 Degrees Of Freedom (6-DOF) rigid body, subject to gravity and actuation forces, is presented. This model is shown to satisfy the necessary conditions to the existence of a non-linear observer (Thau) when proper assumptions for the actuation forces are made. The observer can thus be used to generate diagnostic residuals inside a Fault Detection (FD) system. Finally, the proposed approach is customized for sensor fault detection on an unmanned quad-rotor vehicle, and simulation results show the effectiveness of the adopted solution.

Coordinated fleet of Autonomous Underwater Gliders (AUGs) can provide significant benefit to a number of marine applications including ocean sampling, mapping, surveillance and communication. Traditional techniques for navigating underwater vehicles have been designed for single-vehicle operations and do not scale well to multi-vehicle operations and missions. In this paper a navigation system for a fleet of AUGs is developed based on Networked Decentralized Model Predictive Control (ND-MPC). The proposed approach coordinates a group of point-mass mobile agents to achieve a desired formation, while avoiding collisions between themselves. In order to obtain collision free paths, the approach integrates the required collision avoidance constraints. The fleet localization is performed by sensor fusion using adaptive extended Kalman filtering. The free collision and convergence properties are verified through simulations results. The proposed approach can be generalized to formation of heterogeneous autonomous agents.

A fleet of unmanned marine vehicles moving together in a prescribed pattern forms a robotic system for cooperative tasks. The problem is to control several marine vehicles such that, after transients, they form a required formation and move along a desired trajectory. The capability to accomplish the mission even in case of fault, is a fundamental requirement for these kind of vehicles. A completely decentralized nonlinear predictive control integrated with a fault tolerant strategy is here proposed for allowing safe cooperation. Based both on the local information and the acquired one through a LAN, each marine vehicle selects the desired formation to keep and plans its future actions. The proposed solution can be applied to different classes of unmanned marine vehicles.

This paper proposes an actuator fault-tolerant control scheme designed for a class of nonlinear systems, and then applied to an underwater Remotely Operated Vehicle (ROV) used for inspection purposes. Fault detection is achieved using a Variable Structure System (VSS) observer, while a sliding-mode based approach has been used both for fault isolation and ROV control, after the application of an input decoupling nonlinear state transformation to the ROV model. Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators. An extensive simulation study has been also performed, supporting the effectiveness of the proposed approach.

Combining availability, viability, sustainability, technical options, and environmental impact in an energy-planning project is a difficult job itself for the today’s engineers. This becomes harder if the potential investors also need to be persuaded. Moreover, the problem increases even more if various consumptions are considered, as their patterns depend to a large extent on the type of facility and the activity. It is therefore essential to develop tools to assess the balance between generation and demand in a given installation. In this paper, a valuable tool is developed for the seamless calculation of the integration possibilities of renewable energies and the assessment of derived technical, financial and environmental impacts. Furthermore, it also considers their interaction with the power grid or other networks, raising awareness of the polluting emissions responsible for global warming. Through a series of Structured Query Language databases and a dynamic data parameterization, the software is provided with sufficient information to encode, calculate, simulate and graphically display information on the generation and demand of electric, thermal and transport energy, all in a user-friendly environment, finally providing an evaluation and feasibility report.

This paper proposes an actuator fault-tolerant control scheme, composed of the usual modules performing detection, isolation, accommodation, designed for a class of nonlinear systems, and then applied to an underwater remotely operated vehicle (ROV) used for inspection purposes. Detection is in charge of a residual generation module, while a sliding-mode based approach has been used both for ROV control and fault detection, after the application of an input decoupling nonlinear state transformation to the ROV model. Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators. An extensive simulation study has been also performed, supporting the effectiveness of the proposed approach.

Using the geometric approach, a solution is given to the problem of detecting faults in linear periodic discrete-time systems. The solution consists on developing an unknown input observer. The existing conditions are provided in terms of new geometric notion of outer observable subspace that is the dual notion of inner reachable subspace. Making use of the new notion, a residual generator is developed, such that the residual is made independent of unknown input disturbance, and its output-free response goes asymptotically to zero.

The paper presents a model-based sensor fault detection and isolation system applied in real-time to unmanned ground vehicles. Structural analysis is applied on the nonlinear model of the vehicle for building the residual generation module, followed by an ad-hoc residual evaluation module for detecting single and multiple sensor faults. The overall proposed diagnosis scheme has been tested in real-time on a real mobile robot in an outdoors environment and for different tasks. The obtained experimental results are satisfactory in terms of diagnosis performance and real-time implementation.

This paper addresses the problem of fault detection and isolation for helicopters used as Unmanned Aerial Vehicles. First a model for a reduced-scale helicopter is presented, then this model is linearized and used to design unknown input observers for fault diagnosis. The developed system is tested and analyzed. The results prove that it represents an effective solution to fault diagnosis problems in Unmanned Aerial Vehicles.

A fleet of Autonomous Underwater Vehicles (AUVs) moving together in a prescribed pattern can form an efficient data acquisition network. The problem is to control several AUVs such that after transients, they form a required formation and move along a desired trajectory. The capability to accomplish the mission even in case of faults, is a fundamental requirement for these kind of missions. A completely decentralized predictive control and FDI strategy is here proposed for allowing cooperation. Trough an underwater communication channel, each vehicle broadcasts its position, its future behavior and its actuator/sensor fault situation. Based both on the local and the received information, each vehicle selects the desired formation to keep and plans its future actions. Simulation results are provided to validate the approach.

Nutrition plays a key role in both the prevention of several critical diseases and lead an healthier life in the modern society. In order to analyse household’s eating habits in the Italian scenario, authors propose the design of an innovative system called “Smart Shelve”. This system consists in a robotic structure integrable with various appliances, such as refrigerators, which allows to receive and deliver several types of foods in order to control their daily consumption. Smart Shelve is equipped with external optical sensors for food recognition, while inside it is composed of several food compartments in which load cells measure the weight of each type of food. The system is capable to warn the users who have recently conducted an incorrect style. The system is also able to send the acquired food consumption information to the cloud in order to provide statistical services regarding the nutrition style of clusters of users localized in a specific geographical area (e.g. district or region).

The size of the Italian expenditure for the health care on Gross Domestic Product (GDP) and on total public expenditure, is in line with those of the major industrialized countries, and somewhat lower than the European average. The Italian healthcare sector does not simply face a contraction of the expenditure: in effect, it should be highlighted that Italy currently does not spend “a lot” and, above all, invests “little” in the mentioned sector. Nevertheless, the health sector can be a very important flywheel for the economic recovery, provided that a lot of attention is paid, and the best resources are invested. In this work, the authors intend developing an analysis of the desirability of investment on Information and Communication Technologies (ICT) in the health sector, and the consequent compatibility with current expenditure, by identifying at the outset the channels multipliers related to the resources at present allocated to the current health expenditure and capital account. For this analysis, two different simulation scenarios have been considered: in the first, an increase in Health services demand by Government has been studied, while in the second, an increase in final demand of Health services by Households has been considered. © Springer International Publishing Switzerland 2016.

ROS framework lacks of an internal tool to design or test control algorithms and therefore developers have to test their algorithms on-line, directly on the robotic platform they are working with. This is not always safe and possible, and a rapid prototyping tool can help during the design phase. Users can develop their algorithms directly on the controller board and safely test them in a simulated scenario. Although some rapid prototyping tools exist in the ROS community, none of them take Simulink® into consideration. In this work the authors provide an open source Rapid Prototyping tool which integrates ROS and Simulink. The proposed package is useful for control designers, who are frequently used to exploit Simulink features for control deployment. The tool can be downloaded from https://github.com/gionatacimini/rapros.

This paper is concerned with the problem of state estimation for the class of linear discrete-time Gaussian systems with intermittent observations due to packet losses. This is a common case in networked control systems, where the state of a remote plant is estimated from measurements carried through a lossy network. We assume that the receiver does not know the sequence of packet dropouts. This is typical, e.g., in wireless sensor networks or in networks that cannot rely on protocols that provide information on packet loss. Moreover, we assume that the sequence of packet dropouts is correlated, thus subsuming both the cases of independent dropouts and dropouts modeled as a Markov chain. We propose a detection-estimation approach to the problem of state estimation. The estimator consists of two stages: the first is a nonlinear optimal detector, which decides if a packet dropout has occurred, and the second is a time-varying Kalman filter, which is fed with both the observations and the decisions from the first stage. The overall estimator has finite memory and the tradeoff between performance and computational complexity can be easily controlled. As a case study, we derive the decision rule in closed form in the case of dropout sequence modeled as a Markov chain. Simulation results highlight the effectiveness of the proposed approach, which outperforms the linear recursive estimator of Hadidi and Schwartz.

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.

Simulators for Wireless Sensor Networks (WSNs) are one of the most important tools for systems development. They enable to study and evaluate new theories and hypotheses for sensors data gathering, testing new applications and protocols. Nowadays, there are a large number of open source WSN simulators and they can be divided into different categories according to their features and main applications. Due to the ability to increase the real WSN prototyping, the Cross Levels Simulator, like Cooja, has become an important class of simulators. Although they are open source, flexible and extensible in all levels, the test interface, the external connection at a physical level and the direct interaction with the process control via the WSN is very poor. In this work we present the Cooja Advanced Sky Interface which is an extension of the Contiki's Cooja network simulator for the Sky mote. Due to the absence of the analog output control in the Contiki OS for the Sky mote, as additional contribution, the Contiki Sky DAC driver has been developed and tested in the Cooja Simulator with the Advanced Sky GUI and GISOO plugin to give the ability to implement control over the wireless sensor network.

Wireless Sensors Network (WSN) integration in a Cyber Physical System (CPS) is becoming one of the most important research topics for increasing the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability in the wireless automation systems. Due the complexity of the CPS, simulators and emulators have to be used to replace the real experiments in order to provide necessary feedback and facilities for this regard. Although the simulators are open source, flexible, extensible and full integrated in a mathematical modelling tools, the external connection at a physical level and the direct interaction with the process control via the WSN in the CPSs is very poor. This paper proposes a new simulation module to control a wireless cyber-physical system, by integrating LabVIEW development environment for a visual programming language from National Instruments, and COOJA, a cross level wireless sensor network simulator. The developed software module, called “GILOO” (Graphical Integration of Labview and cOOja) enables to develop and to debug control policies in a simulated or realistic scenario, using the virtual environment or the hardware module, such as the National Instruments Data Acquisition (SCADA), the FPGA platform, the CompactRio, etc. The designed GILOO module has been experimentally tested and preliminary results are shown in this paper. In detail, a smart home mock-up is proposed to verify its correct behavior and to realize the networked control of an indoor LED lighting system.

Unmanned vehicles operating in formation may perform more complex tasks than vehicles working indi- vidually. In order to control a formation of unmanned vehicles, however, the following main issues must be faced: vehicle motion is usually described by nonlinear models, feasible control actions for each vehicle are constrained, collision between the members of the formation must be avoided while, at the same time, the computational efforts must be kept low due to limitations on the onboard hardware. To solve these problems, a nonlinear decentralized model predictive control algorithm is presented in this paper. The adopted model is based on the nonlinear kinematic equations describing the motion of a body with six degrees of freedom, where each vehicle shares information with its leader only by means of a wireless local area network. Saturation and collision-free constraints are included within the formulation of the optimization problem, while de- centralization allows to distribute the computational efforts amongst all the vehicles of the formation. In order to show the effectiveness of the proposed approach, it has been applied to a formation of quadrotor vehicles. Simulation results prove that the approach presented in this paper is a valid way to solve the problem of controlling a formation of unmanned vehicles, granting at the same time the possibility to deal with constraints and nonlinearity while limiting the computational efforts through decentralization. DOI: http://dx.doi.org/10.5755/j01.itc.44.1.7219

This paper presents a set of results on the design and on the experimental validations of a Sensor Fault Detection System (SFDS) for a smart wheelchair to prevent simples navigation sensor faults and hence to reduce the risk of safety hazards. The design has been performed by the Structural Analysis technique which uses graph theory to determine which redundancy exists in the sensory system and thus shows the possibility to detect and handle the navigation sensor faults. The considered navigation sensor equipment includes odometric, gyroscopic and laser range measures. The experimental tests have been developed by a smart wheelchair equipped with a navigation system.

Physical rehabilitation is an important medical activity sector for the recovery of physical functions and clinical treatment of people affected by different pathologies, as neurodegenerative diseases (i.e. multiple sclerosis, Parkinson and Alzheimer diseases, amyotrophic lateral sclerosis), neuromuscular disorders (i.e. dystrophies, myopathies, amyotrophies and neuropathies), neurovascular disorders/trauma (i.e. stroke and traumatic brain injuries), and mobility for the elderly. During the rehabilitation, the patient has to perform different exercises specific for the own disease: while some exercises have to be performed with specific equipment and under the supervision of professional staff, others can be performed by patients without the supervision of physiotherapists. In this last case, it is possible to reduce the costs of health and care national system and to accomplish the treatment at home. In this work, a computer vision system for physical rehabilitation at home is proposed. The vision system exploits a low cost RGB-D camera and open source libraries for the image processing, in order to monitor the exercises performed by the patients, and returns a video feedback to improve the treatment effectiveness and to increase the user’s motivation, interest, and perseverance. Moreover, the vision system evaluates an exercise score in order to monitor the rehabilitation progress, an helpful information both for the clinician staff and patients, and allow physiotherapists to monitor the patients at home and correct their posture if the exercises are not well performed. This approach has been implemented and experimentally tested using the Microsoft Kinect camera, demonstrating good and reliable performances.

Chronic diseases are an international concern, for their increasing incidence and the strain on individuals and on healthcare systems. In order to enable the healthcare system to cope with increasing demands and to avoid strong decrements in subject’s functionality and well-being, a variety of changes for the management of chronic disease care have been advocated by the World Health Organization. The objective of the research is to verify the feasibility and acceptability of a technological solution that allows to transfer a tailored rehabilitation program for patients with disabilities in the home environment. The first step of the study is to identify the correct technological solution on the basis of the International Standard of Organization (ISO) definition of usability and acceptability. The second step is to verify the capacity of the system to perform correctly what is requested. The third step will be to verify the system efficacy in the patients’ training.

Assistive technologies have the objective to improve the people quality of life of in daily living, with a special aim to those who suffer of physical disabilities or cognitive impairment, which may be caused by an accident, disease or the natural process of ageing. The present paper describes the main results of a study realized for the INTERREG IVC INNOVAGE project, where the domain target addressed are: home and building automation and assistive robotics. The project provides a quick overview of the typical needs of elderly people, describes the state-of-the-art technologies which can be adopted to satisfy these needs and presents a critical analysis of the functionalities, which present and future assistive technologies should possess. The result of this study is a detailed assessments of requirements and limits of nowadays domotics and robotics technologies aimed to improve people quality of life.

In this paper we consider the problem of state estimation for linear discrete-time Gaussian systems with intermittent observations. Intermittent observations result from packet dropouts when data travel along unreliable communication channels, as in the case of wireless sensor networks, or networked control systems. We assume that the receiver does not know the sequence of packet dropouts, which is a common situation, e.g., in wireless sensor networks or in networks that cannot rely on protocols that provide information on packet loss. In this paper we propose a detection-estimation approach to the problem of state estimation. The estimator consists of two stages: the first is a nonlinear optimal detector, which decides if a packet dropout has occurred, and the second is a time-varying Kalman filter, which is fed with both the observations and the decisions from the first stage. The overall estimator has finite memory and the tradeoff between performance and computational complexity can be easily controlled. Simulation results highlight the effectiveness of the proposed approach, which outperforms the linear optimal filter of Nahi. Finally, the method is amenable to generalization.

Both the theoretical background and the experimental results of an algorithm developed to perform human respiratory rate measurements without any physical contact are presented. Based on depth image sensing techniques, the respiratory rate is derived by measuring morphological changes of the chest wall. The algorithm identifies the human chest, computes its distance from the camera and compares this value with the instantaneous distance, discerning if it is due to the respiratory act or due to a limited movement of the person being monitored. To experimentally validate the proposed algorithm, the respiratory rate measurements coming from a spirometer were taken as a benchmark and compared with those estimated by the algorithm. Five tests were performed, with five different persons sat in front of the camera. The first test aimed to choose the suitable sampling frequency. The second test was conducted to compare the performances of the proposed system with respect to the gold standard in ideal conditions of light, orientation and clothing. The third, fourth and fifth tests evaluated the algorithm performances under different operating conditions. The experimental results showed that the system can correctly measure the respiratory rate, and it is a viable alternative to monitor the respiratory activity of a person without using invasive sensors.

This paper proposes a low cost mobile platform for robotic applications which is mainly addressed to educators and developers. Most of robotic models in the market use USB or serial standard protocol to retrieve sensor information, and this can generate delays on sensor readings and degradation of the overall performances. The proposed mobile robotic platform is based instead on the Controller Area Network, namely CAN-bus, which permits to obtain high performances and to increase flexibility and expandability for future technology integration.

The problem of detection and isolation of actuator faults in hexacopter vehicles is address in this article. The aim is to detect and isolate possible faults which can occur on the vehicle actuation system (i.e. rotors and propellers). The dynamic nonlinear model of an hexarotor vehicle is first derived to build a model-based diagnosis system. Then a Thau observer is developed to estimate the states of the hexarotor and, on the usage of this information, to generate the needed set of residuals. The developed fault detection and isolation system is finally tested in a high fidelity hexacopter simulator in which different fault situations are considered.

The goal of this work is the development of a LED driver system for industrial and domestic use, which integrates an AC-DC PFC interleaved boost converter, a resonant DC-DC LLC high efficiency converter, a low-level LED control and a fault detection component. Efficient energy use and harmonics reduction are the main goals to be achieved. AC-DC power stage has been designed to operate in interleaved mode and several control techniques have been tested to increase performances; a linear control has been implemented for the resonant converter; low-level LED control includes a fault detection capabilities. The approaches of simulation and rapid prototyping have been deeply exploited in this work to make faster design and test phases for each individual functional block and to minimize errors. The behaviour of the LED control component has been also compared with a real prototype.

Assistive robotics has the objective to improve the quality of life of people in daily living, with a special aim to those who suffer of physical disabilities or cognitive impairment, which may be caused by an accident, disease or the natural process of ageing. Autonomous mobile robots can be adapted to work as assistive robots, because of their capabilities to navigate in unstructured environments and react to changes in it. In order to turn a mobile robot into an assistive robot, the contest of use and the set of functionalities to perform should be clearly identified, while limiting at the same time hardware complexity and costs, which are key factors against the application of assistive technologies into the real world. The present paper deals with the development of a home robot companion, an assistive robot, which is being realized at Universita Politecnica delle Marche: its main contribution is the implementation of both a stance detection and respiratory rate algorithm by using the information provided by non-invasive sensors, and their integration into a low cost robotic platform which can perform navigation, user identification and tracking. The overall result is an assistive mobile robot which can increase safety at home for the user.

The present paper deals with the development of a Fault Diagnosis and Prognosis supervision module which can be applied to LED lighting systems for both industrial and domestic applications. Hardware redundancy is used for detecting and isolating a fault on the LED lamp (composed of a fixed number of LED strings). Residual signals are obtained as the difference between the currents circulating into two different LED strings piloted by the same input current. A signal-based approach is used for fault detection on the cooling fan subsystem, where the diagnosis system is based on the heat sink temperature. Fault detection is performed through an algorithm based on the trend of the temperature rate on the heat sink. Fault detection on the light, motion and temperature sensors is based on a straightforward evaluation of the sensor readings. In all these cases, a threshold policy has been used to trigger the faulty condition. Finally, the module integrates a model-based prognosis algorithm which is used to predict when LED maintenance should be performed. The proposed solution has been experimentally validated, showing the capability to correctly detect and isolate a fault occurring on the LED string, on the fan subsystem, or on the sensor set, and providing at the same time an estimation of the LED useful life.

We consider the optimal regulator for non-Gaussian discrete-time stochastic systems with a quadratic cost function. We improve the method of [1], where the optimal control is derived from the classical LQG solution by substituting the linear filtering part with a quadratic optimal filter. In this paper we delete the unobservable part of the augmented state space, thus guaranteeing the internal stability of the resulting closed-loop system.

This paper proposes an actuator failure tolerant robust control scheme for underwater Remotely Operated Vehicles (ROVs). A reduced order observer has been introduced first, for estimating the ROV velocities. In order to solve the control problem for the ROV positions, a sliding mode control law has been developed using the available position measurements and the velocity estimates provided by the observer. A thruster failure is shown to be detectable simply checking the presence of any deviation of the observed sliding surfaces. Moreover, an isolation policy for the failed thruster is proposed. Finally, control reconfiguration is performed exploiting the inherent redundancy of actuators. An extensive simulation study has been performed, supporting the effectiveness of the proposed approach.

This paper studies the problem of detecting and isolating faults (FDI) in linear discrete-time periodic systems. The aim is to design an observer-based residual generator where each residual is sensitive to one fault, whilst remaining insensitive to the other faults that can be affected the system. Making use of the geometric tools, and in particular of the outer observable subspace notion, the FDI problem is formulated and necessary and solvability conditions are given. An algorithmic procedure is described to determine the solution of the FDI problem.

This paper addresses the problem of actuator fault detection for a mini-quadrotor. First a model for a four-rotor vehicle, obtained via a Lagrange approach, is presented. In order to stabilize the quadrotor at low cruise speed, a control strategy based on nested saturation controllers is presented. Using a Thau's observer, a diagnostic system has been developed for the nonlinear model of the quadrotor. Different simulation trials have been performed and the analysis of the results proves that the developed diagnostic system represents an effective solution to the problem of actuator fault detection in mini-flying machines.

This paper presents two different approaches for developing the residual generation module of a model-based sensor fault detection (FD) system applied to the robot navigation problem. The first approach is based on the structural analysis. The second one exploits the structure of nonlinear geometric control theory to derive the nonlinear analytical redundancy (NLAR) tests for sensor navigation purposes. The robot sensor suite includes at least a Global Positioning System (GPS) antenna, an Inertial Measurement Unit (IMU), and two incremental optical encoders. Analysis of the residuals generated by these two presented methods shown that the structural analysis is the feasible way to treat the navigation sensor fault detection problem, as confirmed by the developed experimental results.

Today small autonomous helicopters offer a low budget platform for aerial applications such as surveillance (both military and civil), land management and earth sciences. In this paper we introduce a prototype of autonomous aerial vehicle, the Helibot helicopter, specifically designed for applications in cooperative networks. Fundamental steps in the design process of an UAV are shown. We also present work in progress in the field of failure detection and a novel idea to the problem of failure recovery using a terrain vision system.

This paper presents theoretical details of a model-based sensor fault detection and isolation system (SFDIS) applied to unmanned ground vehicles (UGVs). Structural analysis is applied to the nonlinear model of the vehicle for residual generation. Two different solutions have been proposed for developing the residual evaluation module. The vehicle sensor suite includes a global positioning system (GPS) antenna, an inertial measurement unit (IMU), and two incremental optical encoders.

Unmanned Aerial Vehicles represent today an advanced and complex robotics platform for novel tasks. For example, UAVs can be used in applications for traffic monitoring and surveillance, emergency services assistance, photogrammetry and surveying. Generally, an UAV must be fully autonomous; autonomy is accomplished by a complex interconnection of systems related to a wide range of topics, e.g., flight low level control, navigation and task-based planning, elaboration of sensor signals, software architecture for reactive behaviours, communication. Today the challenge is the ability to insert UAVs in a cooperative network based on autonomous agents as UAV, UGV (Unmanned Ground Vehicle) to accomplish a specific task a priori defined. In this paper we introduce a prototype of autonomous aerial vehicle, the Helibot helicopter, specifically designed for applications as surveillance and security.Copyright © 2007 by ASME

This paper presents experimental validation and implementation issues of a model-based sensor fault detection and isolation (FDI) system applied to unmanned ground vehicles (UGVs). Enhanced structural analysis is followed to build the residual generation module, followed by different residual evaluation modules capable of detecting single and multiple sensor faults. The overall proposed sensor fault detection and isolation system (SFDIS) has been tested in real-time on the ATRV-Jr mobile robot when following different trajectories in an outdoors environment. The robot sensor suite includes a Global Positioning System (GPS) antenna, an Inertial Measurement Unit (IMU), and two incremental optical encoders. To increase residual sensitivity to sensor faults, the IMU readings are filtered using a Kalman filter (KF), resulting in a robust and accurate FDI system that detects and isolates single/multiple sensor faults.

This paper presents a preliminary set of results on the design of Actuator Fault Detection Systems (AFDSs) for an underwater remotely operated vehicle (ROV) to prevent actuator faults and hence to reduce the risk of safety hazards. The approach consists of two phases. First a model-based approach is applied to the nonlinear model of the ROV. This solution permits to discover the redundancy of the system, and thus using it for fault detection purposes. Successively, by means of a Sliding Mode Control based approach, the fault is isolated and the control law is reconfigurated using the remaining working actuators. In both the phases the plant is assumed to be affected by “moderate” parametric variations. Moreover, it is supposed to undergo unknown faults, which cause the plant input components to be stuck at some uncertain but bounded time functions.

This paper presents a preliminary set of results on the design of a Sensor Fault Detection System (SFDS) for a smart wheelchair to prevent simples navigation sensor faults and hence to reduce the risk of safety hazards. The Structural Analysis technique is considered which uses graph theory to determine which redundancy exists in the sensory system and thus shows the possibility to detect and handle the navigation sensor faults. The considered navigation sensor equipment includes odometric, gyroscopic and laser range measures.