Plug-and-play distributed state estimation for linear systems

This paper proposes a state estimator for large-scale linear systems described by the interaction of state-coupled subsystems affected by bounded disturbances. We equip each subsystem with a Local State Estimator (LSE) for the reconstruction of the subsystem states using pieces of information from parent subsystems only. Moreover we provide conditions guaranteeing that the estimation errors are confined into prescribed polyhedral sets and converge to zero in absence of disturbances. Quite remarkably, the design of an LSE is recast into an optimization problem that requires data from the corresponding subsystem and its parents only. This allows one to synthesize LSEs in a Plug-and-Play (PnP) fashion, i.e. when a subsystem gets added, the update of the whole estimator requires at most the design of an LSE for the subsystem and its parents. Theoretical results are backed up by numerical experiments on a mechanical system

Host cell wall damage during pathogen infection: mechanisms of perception and role in plant-pathogen interactions

The plant cell wall (CW) is a complex structure that acts as a mechanical barrier, restricting the access to most microbes. Phytopathogenic microorganisms can deploy an arsenal of CWdegrading enzymes (CWDEs) that are required for virulence. In turn, plants have evolved proteins able to inhibit the activity of specific microbial CWDEs, reducing CW damage and favoring the accumulation of CW-derived fragments that act as damage-associated molecular patterns (DAMPs) and trigger an immune response in the host. CW-derived DAMPs might be a component of the complex system of surveillance of CW integrity (CWI), that plants have evolved to detect changes in CW properties. Microbial CWDEs can activate the plant CWI maintenance system and induce compensatory responses to reinforce CWs during infection. Recent evidence indicates that the CWI surveillance system interacts in a complex way with the innate immune system to fine-tune downstream responses and strike a balance between defense and growth

Separating multiscale Battery dynamics and predicting multi-step ahead voltage simultaneously through a data-driven approach

Accurate prediction of battery performance under various ageing conditions is necessary for reliable and stable battery operations. Due to complex battery degradation mechanisms, estimating the accurate ageing level and ageing-dependent battery dynamics is difficult. This work presents a health-aware battery model that is capable of separating fast dynamics from slowly varying states of degradation and state of charge (SOC). The method is based on a sequence-to-sequence learning-based encoder-decoder model, where the encoder infers the slowly varying states as the latent space variables in an unsupervised way, and the decoder provides health-aware multi-step ahead prediction conditioned on slowly varying states from the encoder. The proposed approach is verified on a Lithium-ion battery ageing dataset based on real driving profiles of electric vehicles.Comment: 6 pages, 10 figures, IEEE Vehicle Power and Propulsion confernce(IEEE VPPC 2023

Distributed fault detection and isolation of large-scale nonlinear systems: an adaptive approximation approach

2007/2008The present thesis work introduces some recent and novel results about the problem of fault diagnosis for distributed nonlinear and large scale systems. The problem of automated fault diagnosis and accommodation is motivated by the need to develop more autonomous and intelligent systems that operate reliably in the presence of system faults. In dynamical systems, faults are characterized by critical and unpredictable changes in the system dynamics, thus requiring the design of suitable fault diagnosis schemes. A fault diagnosis scheme that drew considerable attention and provided remarkable results is the so called model based scheme, which is based upon a mathematical model of the healthy behavior of the system that is being monitored. At each time instant, the model is used to compute an estimate of what should be the current behavior of the system, assuming it is not affected by a fault. If the behavior of the system is characterized by the time evolution of its state vector x(t), and the inputs to the system are denoted as u(t), then the most general nonlinear and uncertain discrete time model can be represented by x(t + 1) = f (x(t), u(t)) + η(t) , where the nonlinear function f represents the nominal model of the healthy system, and η(t) is an uncertainty term. A proven way to compute an estimate of the state x(t) is by using a diagnostic observer, so that in healthy conditions the residual between the true and the estimated value is, in practice, close to zero. Should the residual cross at a certain point a suitable threshold ̄ǫ(t), the observed difference between the model estimate and the actual measurements will be explained by the presence of a fault. The model-based scheme outlined so far has showed many interesting properties and advantages over signal-based ones, but anyway poses practical implementation problems when one tries to apply it to actual distributed, large-scale systems. In fact an implicit assumption about the model-based scheme is that the task of measuring all the state and input vectors components, and the task of computing the estimate of x(t) can be done in real-time by some single and powerful computer. But for large enough systems, this assumptions cannot be fulfilled by available measurement, communication and computation hardware. This problem constitutes the motivation of the present work. It will be solved by developing decomposition strategies in order to break down the original centralized diagnosis problem into many distributed diagnosis subproblems, that are tackled by agents called Local Fault Diagnosers that have a limited view about the system, but that are allowed to communicate between neighboring agents. In order to take advantage of the distributed nature of the proposed schemes, the agents are allowed to cooperate on the diagnosis of parts of the system shared by more than one diagnoser, by using consensus techniques. Chapter 2 introduces the problem of model-based fault diagnosis by presenting recent results about the centralized diagnosis of uncertain nonlinear discrete time systems. The development of a distributed fault diagnosis architecture is covered in the key Chapter 3, while Chapters 4 and 5 show how this distributed architecture is implemented for discrete and continuous time nonlinear and uncertain large–scale systems. In every chapter an illustrative example is provided, as well as analytical results that characterize the performances attainable by the proposed architecture. ---------------------------------------------------Questo lavoro di tesi presenta alcuni risultati recenti ed innovativi sulla diagnostica di guasto per sistemi nonlineari distribuiti e su larga scala. Il problema della diagnostica automatica di guasto è motivata dal bisogno di sviluppare sistemi maggiormenti autonomi e robusti, che possano operare in modo affidabile anche in presenza di guasti. Nei sistemi dinamici, i guasti sono caratterizati da variazioni critiche ed imprevedibili della dinamica, e richiedono perciò la progettazione di schemi di diagnostica adeguati. Uno schema che ha riscosso notevole successo è il cosidetto schema basato su modello, che si fonda su un modello matematico del comportamento sano del sistema sotto osservazione. Ad ogni istante, il modello è usato per calcolare una stima di quello che dovrebbe essere il comportamento attuale, supponendo l’assenza di guasti. Se il comportamento del sistema è caratterizzato attraverso l’evoluzione temporale del vettore di stato x(t), ed il vettore degli ingressi è indicato con u(t), allora il modello più generale per un sistema non lineare ed incerto a tempo discreto è x(t + 1) = f (x(t), u(t)) + η(t) , dove la funzione nonlineare f rappresenta la dinamica del sistema sano, mentre η(t) è l’incertezza di modello. Un modo comprovato per calcolare una stima dello stato x(t) fa uso di un osservatore diagnostico, cosicché in condizioni normali il residuo tra il valore vero e quello stimato è, in pratica, quasi nullo. Se dovesse ad un certo punto superare un’opportuna soglia, la differenza osservata tra la stima del modello ed il valore vero misurato sarebbe spiegabile con la presenza di un guasto. Lo schema basato su modello riassunto finora ha mostrato molte proprietà interessanti e vantaggi rispetto quelli basati su segnali, ma pone in ogni caso problemi di tipo pratico quando lo si voglia applicare a sistemi reali distribuiti e su larga scala. Infatti un’ipotesi sottointesa dello schema basato su modello è che il compito di misurare tutte le componenti di x(t) e di u(t), e quello di calcolare la stima di x(t) possa essere portato a termine in tempo reale da un singolo nodo di calcolo. Nel caso di sistemi sufficientemente vasti, però, questa ipotesi non può essere rispettata da alcuna delle risorse di calcolo disponibili in pratica. Questo problema è alla base del presente lavoro di tesi. Verrà risolto sviluppando delle strategie di decomposizione in modo da suddividere il problema di diagnostica centralizzato in molteplici sotto-problemi distribuiti, dati in carico ad agenti detti Diagnostici Locali, che hanno una visione limitata del sistema, ma che possono comunicare con agenti vicini. In modo da sfruttare la natura distribuita dello schema proposto, gli agenti potranno cooperare sulla diagnostica di parti del sistema che siano comuni a più diagnostici, attraverso tecniche di consenso. Il Capitolo 2 introduce il problema della diagnostica basata su modello attraverso dei risultati recenti sulla diagnostica centralizzata di sistemi a tempo discreto con dinamica non lineare ed incerta. Lo sviluppo dell’architettura di diagnostica distribuita è trattato nel fondamentale Capitolo 3, mentre i Capitoli 4 e 5 mostrano come questa architettura distribuita è implementata a tempo discreto e a tempo continuo. In ogni capitolo è presente un esempio didattico, oltre a risultati analitici che caratterizzano le prestazioni ottenibili dall’architettura proposta.XX Ciclo197

Funzioni statistiche per SBBL

È giunta a conclusione la realizzazione in rete del Catalogo Collettivo dei Periodici delle Biblioteche afferenti a SBBL (Sistema Bibliotecario Biomedico Lombardo) con il rilascio delle funzionalità statistiche utili al monitoraggio delle attività di document delivery del gruppo

Identification of genetic network dynamics with unate structure

Motivation: Modern experimental techniques for time course measurement of gene expression enable the identification of dynamical models of genetic regulatory networks. In general, identification involves fitting appropriate network structures and parameters to the data. For a given set of genes, exploring all possible network structures is clearly prohibitive. Modelling and identification methods for the a priori selection of network structures compatible with biological knowledge and experimental data are necessary to make the identification problem tractable. Results: We propose a differential equation modelling framework where the regulatory interactions among genes are expressed in terms of unate functions, a class of gene activation rules commonly encountered in Boolean network modelling. We establish analytical properties of the models in the class and exploit them to devise a two-step procedure for gene network reconstruction from product concentration and synthesis rate time series. The first step isolates a family of model structures compatible with the data from a set of most relevant biological hypotheses. The second step explores this family and returns a pool of best fitting models along with estimates of their parameters. The method is tested on a simulated network and compared with state-of-the-art network inference methods on the benchmark synthetic network IRMA. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Hybrid Design of Multiplicative Watermarking for Defense Against Malicious Parameter Identification

Watermarking is a promising active diagnosis technique for detection of highly sophisticated attacks, but is vulnerable to malicious agents that use eavesdropped data to identify and then remove or replicate the watermark. In this work, we propose a hybrid multiplicative watermarking (HMWM) scheme, where the watermark parameters are periodically updated, following the dynamics of the unobservable states of specifically designed piecewise affine (PWA) hybrid systems. We provide a theoretical analysis of the effects of this scheme on the closed-loop performance, and prove that stability properties are preserved. Additionally, we show that the proposed approach makes it difficult for an eavesdropper to reconstruct the watermarking parameters, both in terms of the associated computational complexity and from a systems theoretic perspective.Comment: 8 pages, first submission to the 62nd IEEE Conference on Decision and Contro

Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines

Wind turbine (WT) controllers are often geared towards maximum power extraction, while suitable operating constraints should be guaranteed such that WT components are protected from failures. Control strategies can be also devised to reduce the generated power, for instance to track a power reference provided by the grid operator. They are called down-regulation strategies and allow to balance power generation and grid loads, as well as to provide ancillary grid services, such as frequency regulation. Although this balance is limited by the wind availability and grid demand, the quality of wind energy can be improved by introducing down-regulation strategies that make use of the kinetic energy of the turbine dynamics. This paper shows how the kinetic energy in the rotating components of turbines can be used as an additional degree-of-freedom by different down-regulation strategies. In particular we explore the power tracking problem based on convex model predictive control (MPC) at a single wind turbine. The use of MPC allows us to introduce a further constraint that guarantees flow stability and avoids stall conditions. Simulation results are used to illustrate the performance of the developed down-regulation strategies. Notably, by maximizing rotor speeds, and thus kinetic energy, the turbine can still temporarily guarantee tracking of a given power reference even when occasional saturation of the available wind power occurs. In the study case we proved that our approach can guarantee power tracking in saturated conditions for 10 times longer than with traditional down-regulation strategies.Comment: 6 pages, 2 figures, 61st IEEE Conference on Decision and Control 202

Impact of Sub-Ambient Temperature on Aging Rate and Gas Separation Properties of Polymers of Intrinsic Microporosity

Aging in polymers of intrinsic microporosity has slowed exploitation due to a decay in performance over time since densification makes them unsuitable for industrial applications. This work aimed to study the impact of the operation and storage temperature on the gas separation properties and aging rates of PIM-1 self-standing films. The permeability, diffusivity, and solubility of the tested membranes were monitored through permeation tests for pure carbon dioxide and nitrogen at a maximum upstream pressure of 1.3 bar for temperatures ranging from −20 °C to 25 °C. This study found significant benefits in the operation of glassy polymeric membranes at low temperatures, resulting in a favourable trade-off in separation performance and a reduction in the aging rate by three orders of magnitude. This brings new opportunities for the industrial application of PIMs in innovative carbon capture processes

Performance assessment of ferrite- and neodymiumassisted synchronous reluctance machines

Growing attention towards environmental sustainability of energy conversion and stricter efficiency standards are encouraging the market penetration of high-efficiency electrical motors. Current regulations define international efficiency classes and the testing procedures for direct-on-line machines only, commonly induction motors. Synchronous reluctance machines are a valid alternative to the widely employed induction motors for variable-speed applications, due to their low manufacturing cost and higher efficiency. With proper design, torque ripple can be mitigated as much as to make rotor skewing unnecessary for most of applications. The low power factor downside can be fixed by inserting low-cost ferrite magnet into the rotor barriers, with benefits also on the torque capability and constant power speed range. The aim of this paper is to assess the performance and efficiency potential of one synchronous reluctance and two permanent magnet-assisted synchronous reluctance machine prototypes, obtained by replacing the rotor of a general-purpose induction motor with the said synchronous reluctance ones. The rotor barriers have been designed by means of a genetic optimization algorithm has and then adapted to insert commercially available magnets, compliant with minimum extracost requirements. The two prototypes were comprehensively characterized, to validate the design phase and to investigate the performance of the machines. The provided experimental results are critically examined and commented