Execution Time of Optimal Controls in Hard Real Time, a Minimal Execution Time Solution for Nonlinear SDRE

Many engineering fields, such as automotive, aerospace, and the emerging challenges towards industry 4.0, have to deal with Real-Time (RT) or Hard Real Time (HRT) systems, where temporal constraints must be fulfilled, to avoid critical behaviours or unacceptable system failures. For this reason, estimation of code's Worst-Case Execution Time (WCET) has received lots attention because in RT systems a fundamental requirement is to guarantee at least a temporal upper bound of the code execution for avoiding any drawbacks. However, until now there is no approved method to compute extremely tight WCET. Nowadays, indeed, HRT requirements are solved via hardware, using multi-cores embedded boards that allow the computation of the deterministic Execution Time (ET). The availability of these embedded architectures has encouraged the designers to look towards more computationally demanding optimal control techniques for RT scenarios, and to compare and analyze performances also evaluating a tight WCET. However, this area still lacks deep investigations. This paper has the intent of analysing results regarding the choice between three of the most established optimal controls (LQR, MPC, SDRE), providing the first link between WCET analysis and control algorithms performances. Moreover, this work shows how it is also possible to obtain a minimal ET solution for the nonlinear SDRE controller. The results might be useful for future implementations and for coping with Industry 4.0 emerging challenges. Furthermore, this approach can be useful in control system engineering field, especially in the design stage for RT or HRT systems, where temporal bounds have to be fulfilled jointly with all the other application's specifications

On Condition Maintenance for Robotics and Machines in Industry 4.0 scenarios

Un concetto fondamentale nell' Industria 4.0 è la cooperazione tra la produzione e la pianificazione della manutenzione poiché ciò consente di ottenere una manutenzione efficiente che consente alle aziende di implementare un sistema di produzione economicamente vantaggioso. A tal proposito, la prevenzione nella manutenzione ha riscosso molto interesse da parte della ricerca e delle industrie, dato che può portare enormi vantaggi tecnici ed economici. Il Condition Monitoring (CM) ha un ruolo chiave nell'ottenere sistemi affidabili di manutenzione preventiva perché, monitorando i componenti, è possibile valutare le condizioni fisiche, analizzare e decidere possibili successive azioni di manutenzione. Tuttavia, il CM per la Robotica o Macchinari complessi sono ancora in fase di studio studio poiché, in sistemi così complessi, definire una strategia per rilevare e diagnosticare automaticamente i guasti non è semplice. Questo perché ogni sistema ha le sue peculiarità e logiche di funzionamento, quindi definire un'architettura per supervisionare lo stato di salute dei componenti richiede una profonda comprensione del processo. Pertanto non è facile definire un approccio generale che possa essere riutilizzato e adattato a diversi casi di studio. Infatti, la maggior parte delle soluzioni proposte in letteratura soffre della possibilità di essere generalizzata ed applicata in un contesto diverso. Per questo motivo, il focus di questa Tesi è quello di affrontare il tema della CM per la Robotica e Macchine industriali studiando il comportamento dei segnali dei motori elettrici per capire come identificare le caratteristiche difettose in caso di malfunzionamenti o guasti in componenti oltre il motore stesso. Infatti, considerando che la maggior parte dei sistemi industriali è composta principalmente da motori elettrici e sistemi di trasmissione, l'idea di base è quella di studiare il comportamento dei segnali del motore più ricchi di informazioni per rilevare e diagnosticare guasti, testandone poi l'efficacia in diversi casi studio reali. Più in dettaglio, questa tesi si propone di indagare il comportamento dei segnali di corrente e coppia per il rilevamento e la diagnosi dei guasti nei robot e macchinari che lavorano in condizioni non stazionarie (velocità e carico variabili). In particolare, le correnti sono state utilizzate per valutare la salute dei sistemi di trasmissione a cinghia in un robot cartesiano e in una macchina per l'imballaggio industriale, mentre una nuova metodologia per il rilevamento e la diagnosi dei guasti che utilizza la stima della coppia di carico del motore per applicazioni con variazioni di velocità e condizioni di carico sconosciute è stato proposta per monitorare sistemi di trasmissione relativamente breve. Entrambe le soluzioni sono state sviluppate con l'idea di essere adattabili a diverse applicazioni con il fine di valutare lo stato di salute del sistema di trasmissione. Infine, per concludere, sono state evidenziate le difficoltà nella definizione di architetture per la CM nei manipolatori collaborativi utilizzati per compiti dinamici ed è stata proposta soluzione che può essere considerata valida per diversi scenari. Quest'ultima soluzione è utile per superare i limiti attuali relativi alla definizione di algoritmi per il rilevamento automatico dei guasti nei robot collaborativi che lavorano nella produzione flessibile.One priority aspect of Industry 4.0 is the cooperation between production and maintenance planning since this permits obtaining efficient maintenance allowing companies to implement a cost-effective production system. In this regard, prevention in maintenance has received a lot of interest from research and industries, given that, may bring huge technical and economical advantages. Condition Monitoring (CM) has a key role in obtaining reliable preventive maintenance systems because, by monitoring components, is possible to assess physical conditions, analysis and possible subsequent maintenance actions. However, solutions for the application of effective CM in Robotics or complex Machines are still under investigation since, in such complex systems, defining a strategy to automatically detect and diagnose failures is not straightforward. This is because every system has its own peculiarities and working logic, thus defining an architecture to supervise components’ health requires a deep understanding of the process. Therefore it is not easy to define a general approach which can be reused and adapted to different case studies. In fact, most of the proposed solutions in the literature suffer from the possibility of being generalized and applied to a different scenario. For this reason, the focus of this Thesis will be on approaching the theme of CM in Robotics and Machines by studying electric motors signals behaviour in order to understand how to identify faulty features in the case of malfunctions or failures in components beyond the motor itself. In fact, considering that most industrial systems are mainly composed of electric motors and transmission systems, the basic idea is to study the behaviour of the motor signals most rich in information to detect and diagnose failures, testing then its effectiveness in different real case studies. More in detail, this Thesis aims to investigate the behaviour of Current and Torque signals for Fault Detection and Diagnosis (FDD) in Robots and Machines working under non-stationary conditions (varying speed and load). In particular, Currents have been used to assess belt-drive systems health in a Cartesian Robot (CR) and in an industrial packaging machine while a novel FDD methodology using Motor load Torque (MlT) estimation for applications with speed variations and unknown load conditions has been proposed to monitor relatively short-drive trains. Both solutions have been developed with the idea of being adaptable to different applications which are necessary to evaluate transmission system health. Lastly, to conclude have been pointed out difficulities in defining CM in collaborative manipulators used for dynamics tasks and has been proposed a general CM architecture which can be considered valid for different case scenarios. This last solution is helpful in overcoming current limits regarding the definition of algorithms for automatic FDD in collaborative robots (cobots) working on flexible manufacturing

ROS-Based Condition Monitoring Architecture Enabling Automatic Faults Detection in Industrial Collaborative Robots

The Condition Monitoring (CM) of industrial collaborative robots (cobots) has the potential to decrease downtimes in highly automated production systems. However, in such complex systems, defining a strategy for effective CM and automatically detecting failures is not straightforward. In this paper, common issues related to the application of CM to collaborative manipulators are first introduced, discussed, and then, a solution based on the Robot Operating System (ROS) is proposed. The content of this document is highly oriented towards applied research and the novelty of this work mainly lies in the proposed CM architecture, while the methodology chosen to assess the manipulator’s health is based on previous research content. The CM architecture developed and the relative strategy used to process data are useful for the definition of algorithms for the automatic detection of failures. The approach is based on data labeling and indexing and aims to extract comparable data units to easily detect possible failure. The end of this paper is provided with a proof of concept (PoC) applied to an industrial collaborative manipulator where the proposed CM strategy has been implemented and tested in a real application scenario. Finally, it is shown how the proposed methodology enables the possibility of defining standard Health Indicators (HIs) to detect joint anomalies using torque information even under a highly dynamic and non-stationary environmental conditions

Comparison of PMSMs Motor Current Signature Analysis and Motor Torque Analysis Under Transient Conditions

PMSMs are widely used in applications on electric vehicles, robotics and mechatronic systems of industrial machinery. Thus it becomes increasingly interesting to prevent their fault or malfunctioning with Predictive Maintenance (PdM). However, reaching this outcome could be difficult, especially if the stationary condition is not achieved and without additional sensors. This paper examines the use of a load torque observer based on Extended Kalman Filter for the diagnosis of electric drives working under non-stationary conditions. The proposed Motor Torque Analysis (MTA) is compared with the Motor Current Signature Analysis by evaluating their diagnostic capabilities under the assumed conditions. Finally, the results of bearing failure detection under non-stationary conditions are presented, highlighting the superior diagnostic capabilities of the MTA under such conditions

Human-Robot Perception in Industrial Environments: A Survey

Perception capability assumes significant importance for human–robot interaction. The forthcoming industrial environments will require a high level of automation to be flexible and adaptive enough to comply with the increasingly faster and low-cost market demands. Autonomous and collaborative robots able to adapt to varying and dynamic conditions of the environment, including the presence of human beings, will have an ever-greater role in this context. However, if the robot is not aware of the human position and intention, a shared workspace between robots and humans may decrease productivity and lead to human safety issues. This paper presents a survey on sensory equipment useful for human detection and action recognition in industrial environments. An overview of different sensors and perception techniques is presented. Various types of robotic systems commonly used in industry, such as fixed-base manipulators, collaborative robots, mobile robots and mobile manipulators, are considered, analyzing the most useful sensors and methods to perceive and react to the presence of human operators in industrial cooperative and collaborative applications. The paper also introduces two proofs of concept, developed by the authors for future collaborative robotic applications that benefit from enhanced capabilities of human perception and interaction. The first one concerns fixed-base collaborative robots, and proposes a solution for human safety in tasks requiring human collision avoidance or moving obstacles detection. The second one proposes a collaborative behavior implementable upon autonomous mobile robots, pursuing assigned tasks within an industrial space shared with human operators

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field