Mouse Housing System Using Pressurized Cages Intraventilated by Direct-Current Microfans

We performed the initial assessment of an alternative pressurized intraventilated (PIV) caging system for laboratory mice that uses direct-current microfans to achieve cage pressurization and ventilation. Twenty-nine pairs of female SPF BALB/c mice were used, with 19 experimental pairs kept in Ply cages and 10 control pairs kept in regular filter-top (FT) cages. Both groups were housed in a standard housing room with a conventional atmospheric control system. For both systems, intracage temperatures were in equilibrium with ambient room temperature. PIV cages showed a significant difference in pressure between days 1 and 8. Air speed (and consequently airflow rate) and the number of air changes hourly in the PIV cages showed decreasing trends. In both systems, ammonia concentrations increased with time, with significant differences between groups starting on day 1. Overall, the data revealed that intracage pressurization and ventilation by using microfans is a simple, reliable system, with low cost, maintenance requirements, and incidence of failures. Further experiments are needed to determine the potential influence of this system on the reproductive performance and pulmonary integrity in mice.

OPTIMIZATION AND CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

Optimization and control of a continuous polymerization reactor

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

Simulatore di interfaccia uomo-macchina per i controllo di UAV

Recent statistics have demonstrated that two of the most important causes of failures of the UAVs (Uninhabited Aerial Vehicle) missions are related to the low level of decisional autonomy of vehicles and to the man machine interface. Therefore, a relevant issue is to design a display/controls architecture which allows the efficient interaction between the operator and the remote vehicle and to develop a level of automation which allows the vehicle the decision about change in mission. The research presented in this paper focuses on a modular man-machine interface simulator for the UAV control, which simulates UAV missions, developed to experiment solution to this problem. The main components of the simulator are an advanced interface and a block defined automation, which comprehend an algorithm that implements the level of automation of the system. The simulator has been designed and developed following a user-centred design approach in order to take into account the operator’s needs in the communication with the vehicle. The level of automation has been developed following the supervisory control theory which says that the human became a supervisor who sends high level commands, such as part of mission, target, constraints, in then-rule, while the vehicle receives, comprehends and translates such commands into detailed action such as routes or action on the control system. In order to allow the vehicle to calculate and recalculate the safe and efficient route, in term of distance, time and fuel a 3D planning algorithm has been developed. It is based on considering UASs representative of real world systems as objects moving in a virtual environment (terrain, obstacles, and no fly zones) which replicates the airspace. Original obstacle avoidance strategies have been conceived in order to generate mission planes which are consistent with flight rules and with the vehicle performance constraints. The interface is based on a touch screen, used to send high level commands to the vehicle, and a 3D Virtual Display which provides a stereoscopic and augmented visualization of the complex scenario in which the vehicle operates. Furthermore, it is provided with an audio feedback message generator. Simulation tests have been conducted with pilot trainers to evaluate the reliability of the algorithm and the effectiveness and efficiency of the interface in supporting the operator in the supervision of an UAV mission. Results have revealed that the planning algorithm calculate very efficient routes in few seconds, an adequate level of workload is required to command the vehicle and that the 3D based interface provides the operator with a good sense of presence and enhances his awareness of the mission scenario and of the vehicle under his control.

Aportaciones al control inverso con modelo de referencia basado en lógica borrosa, redes neuronales y algoritmos genéticos

Programa de doctorado: Ingeniería de Telecomunicación Avanzada.

Induction Motor Diagnosis in Variable Speed Drives

Several diagnostic techniques are presented for the detection of electrical fault in induction motor variable speed drives. These techinques are developed taking into account the impact of the control system on machine variables and non stationary operating conditions.

Model predictive flight control systems for rotorcraft UAS

Constraints are widely present in the flight control problems: actuators saturations or flight envelope limitations are only some examples of that. The ability of Model Predictive Control (MPC) of dealing with the constraints joined with the increased computational power of modern calculators makes this approach attractive also for fast dynamics systems such as agile air vehicles. This PhD thesis presents the results, achieved at the Aerospace Engineering Department of the University of Bologna in collaboration with the Dutch National Aerospace Laboratories (NLR), concerning the development of a model predictive control system for small scale rotorcraft UAS. Several different predictive architectures have been evaluated and tested by means of simulation, as a result of this analysis the most promising one has been used to implement three different control systems: a Stability and Control Augmentation System, a trajectory tracking and a path following system. The systems have been compared with a corresponding baseline controller and showed several advantages in terms of performance, stability and robustness.

Controllo Vettoriale generalizzato per Macchine Elettriche Trifase

Nell’ambito della presente tesi verrà descritto un approccio generalizzato per il controllo delle macchine elettriche trifasi; la prima parte è incentrata nello sviluppo di una metodologia di modellizzazione generale, ossia in grado di descrivere, da un punto di vista matematico, il comportamento di una generica macchina elettrica, che possa quindi includere in sé stessa tutte le caratteristiche salienti che possano caratterizzare ogni specifica tipologia di macchina elettrica. Il passo successivo è quello di realizzare un algoritmo di controllo per macchine elettriche che si poggi sulla teoria generalizzata e che utilizzi per il proprio funzionamento quelle grandezze offerte dal modello unico delle macchine elettriche. La tipologia di controllo che è stata utilizzata è quella che comunemente viene definita come controllo ad orientamento di campo (FOC), per la quale sono stati individuati degli accorgimenti atti a migliorarne le prestazioni dinamiche e di controllo della coppia erogata. Per concludere verrà presentata una serie di prove sperimentali con lo scopo di mettere in risalto alcuni aspetti cruciali nel controllo delle macchine elettriche mediante un algoritmo ad orientamento di campo e soprattutto di verificare l’attendibilità dell’approccio generalizzato alle macchine elettriche trifasi. I risultati sperimentali confermano quindi l’applicabilità del metodo a diverse tipologie di macchine (asincrone e sincrone) e sono stati verificate nelle condizioni operative più critiche: bassa velocità, alta velocità bassi carichi, dinamica lenta e dinamica veloce.

Nonlinear Control of Magnetically Actuated Spacecraft

The topic of this thesis is the feedback stabilization of the attitude of magnetically actuated spacecraft. The use of magnetic coils is an attractive solution for the generation of control torques on small satellites flying inclined low Earth orbits, since magnetic control systems are characterized by reduced weight and cost, higher reliability, and require less power with respect to other kinds of actuators. At the same time, the possibility of smooth modulation of control torques reduces coupling of the attitude control system with flexible modes, thus preserving pointing precision with respect to the case when pulse-modulated thrusters are used. The principle based on the interaction between the Earth's magnetic field and the magnetic field generated by the set of coils introduces an inherent nonlinearity, because control torques can be delivered only in a plane that is orthogonal to the direction of the geomagnetic field vector. In other words, the system is underactuated, because the rotational degrees of freedom of the spacecraft, modeled as a rigid body, exceed the number of independent control actions. The solution of the control issue for underactuated spacecraft is also interesting in the case of actuator failure, e.g. after the loss of a reaction-wheel in a three-axes stabilized spacecraft with no redundancy. The application of well known control strategies is no longer possible in this case for both regulation and tracking, so that new methods have been suggested for tackling this particular problem. The main contribution of this thesis is to propose continuous time-varying controllers that globally stabilize the attitude of a spacecraft, when magneto-torquers alone are used and when a momentum-wheel supports magnetic control in order to overcome the inherent underactuation. A kinematic maneuver planning scheme, stability analyses, and detailed simulation results are also provided, with new theoretical developments and particular attention toward application considerations.

Control of protein degradation pathways by BAG proteins and changes during aging

Many age-related neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and polyglutamine disorders, including Huntington’s disease, are associated with the aberrant formation of protein aggregates. These protein aggregates and/or their precursors are believed to be causally linked to the pathogenesis of such protein conformation disorders, also referred to as proteinopathies. The accumulation of protein aggregates, frequently under conditions of an age-related increase in oxidative stress, implies the failure of protein quality control and the resulting proteome instability as an upstream event of proteinopathies. As aging is a main risk factor of many proteinopathies, potential alterations of protein quality control pathways that accompany the biological aging process could be a crucial factor for the onset of these disorders.rnrnThe focus of this dissertation lies on age-related alterations of protein quality control mechanisms that are regulated by the co-chaperones of the BAG (Bcl-2-associated athanogene) family. BAG proteins are thought to promote nucleotide exchange on Hsc/Hsp70 and to couple the release of chaperone-bound substrates to distinct down-stream cellular processes. The present study demonstrates that BAG1 and BAG3 are reciprocally regulated during aging leading to an increased BAG3 to BAG1 ratio in cellular models of replicative senescence as well as in neurons of the aging rodent brain. Furthermore, BAG1 and BAG3 were identified as key regulators of protein degradation pathways. BAG1 was found to be essential for effective degradation of polyubiquitinated proteins by the ubiquitin/proteasome system, possibly by promoting Hsc/Hsp70 substrate transfer to the 26S proteasome. In contrast, BAG3 was identified to stimulate the turnover of polyubiquitinated proteins by macroautophagy, a catabolic process mediated by lysosomal hydrolases. BAG3-regulated protein degradation was found to depend on the function of the ubiquitin-receptor protein SQSTM1 which is known to sequester polyubiquitinated proteins for macroautophagic degradation. It could be further demonstrated that SQSTM1 expression is tightly coupled to BAG3 expression and that BAG3 can physically interact with SQSTM1. Moreover, immunofluorescence-based microscopic analyses revealed that BAG3 co-localizes with SQSTM1 in protein sequestration structures suggesting a direct role of BAG3 in substrate delivery to SQSTM1 for macroautophagic degradation. Consistent with these findings, the age-related switch from BAG1 to BAG3 was found to determine that aged cells use the macroautophagic system more intensely for the turnover of polyubiquitinated proteins, in particular of insoluble, aggregated quality control substrates. Finally, in vivo expression analysis of macroautophagy markers in young and old mice as well as analysis of the lysosomal enzymatic activity strongly indicated that the macroautophagy pathway is also recruited in the nervous system during the organismal aging process.rnrnTogether these findings suggest that protein turnover by macroautophagy is gaining importance during the aging process as insoluble quality control substrates are increasingly produced that cannot be degraded by the proteasomal system. For this reason, a switch from the proteasome regulator BAG1 to the macroautophagy stimulator BAG3 occurs during cell aging. Hence, it can be concluded that the BAG3-mediated recruitment of the macroauto-phagy pathway is an important adaptation of the protein quality control system to maintain protein homeostasis in the presence of an enhanced pro-oxidant and aggregation-prone milieu characteristic of aging. Future studies will explore whether an impairment of this adaptation process may contribute to age-related proteinopathies.

Lernen mit einer Hybride aus Lernendem Klassifizierendem System und Selbstorganisierender Karte

Im Forschungsgebiet der Künstlichen Intelligenz, insbesondere im Bereich des maschinellen Lernens, hat sich eine ganze Reihe von Verfahren etabliert, die von biologischen Vorbildern inspiriert sind. Die prominentesten Vertreter derartiger Verfahren sind zum einen Evolutionäre Algorithmen, zum anderen Künstliche Neuronale Netze. Die vorliegende Arbeit befasst sich mit der Entwicklung eines Systems zum maschinellen Lernen, das Charakteristika beider Paradigmen in sich vereint: Das Hybride Lernende Klassifizierende System (HCS) wird basierend auf dem reellwertig kodierten eXtended Learning Classifier System (XCS), das als Lernmechanismus einen Genetischen Algorithmus enthält, und dem Wachsenden Neuralen Gas (GNG) entwickelt. Wie das XCS evolviert auch das HCS mit Hilfe eines Genetischen Algorithmus eine Population von Klassifizierern - das sind Regeln der Form [WENN Bedingung DANN Aktion], wobei die Bedingung angibt, in welchem Bereich des Zustandsraumes eines Lernproblems ein Klassifizierer anwendbar ist. Beim XCS spezifiziert die Bedingung in der Regel einen achsenparallelen Hyperquader, was oftmals keine angemessene Unterteilung des Zustandsraumes erlaubt. Beim HCS hingegen werden die Bedingungen der Klassifizierer durch Gewichtsvektoren beschrieben, wie die Neuronen des GNG sie besitzen. Jeder Klassifizierer ist anwendbar in seiner Zelle der durch die Population des HCS induzierten Voronoizerlegung des Zustandsraumes, dieser kann also flexibler unterteilt werden als beim XCS. Die Verwendung von Gewichtsvektoren ermöglicht ferner, einen vom Neuronenadaptationsverfahren des GNG abgeleiteten Mechanismus als zweites Lernverfahren neben dem Genetischen Algorithmus einzusetzen. Während das Lernen beim XCS rein evolutionär erfolgt, also nur durch Erzeugen neuer Klassifizierer, ermöglicht dies dem HCS, bereits vorhandene Klassifizierer anzupassen und zu verbessern. Zur Evaluation des HCS werden mit diesem verschiedene Lern-Experimente durchgeführt. Die Leistungsfähigkeit des Ansatzes wird in einer Reihe von Lernproblemen aus den Bereichen der Klassifikation, der Funktionsapproximation und des Lernens von Aktionen in einer interaktiven Lernumgebung unter Beweis gestellt.

Non-Linear Analysis and Design of Synchronous Bearingless Multiphase Permanent Magnet Machines and Drives

A two-dimensional model to analyze the distribution of magnetic fields in the airgap of a PM electrical machines is studied. A numerical algorithm for non-linear magnetic analysis of multiphase surface-mounted PM machines with semi-closed slots is developed, based on the equivalent magnetic circuit method. By using a modular structure geometry, whose the basic element can be duplicated, it allows to design whatever typology of windings distribution. In comparison to a FEA, permits a reduction in computing time and to directly changing the values of the parameters in a user interface, without re-designing the model. Output torque and radial forces acting on the moving part of the machine can be calculated. In addition, an analytical model for radial forces calculation in multiphase bearingless Surface-Mounted Permanent Magnet Synchronous Motors (SPMSM) is presented. It allows to predict amplitude and direction of the force, depending on the values of torque current, of levitation current and of rotor position. It is based on the space vectors method, letting the analysis of the machine also during transients. The calculations are conducted by developing the analytical functions in Fourier series, taking all the possible interactions between stator and rotor mmf harmonic components into account and allowing to analyze the effects of electrical and geometrical quantities of the machine, being parametrized. The model is implemented in the design of a control system for bearingless machines, as an accurate electromagnetic model integrated in a three-dimensional mechanical model, where one end of the motor shaft is constrained to simulate the presence of a mechanical bearing, while the other is free, only supported by the radial forces developed in the interactions between magnetic fields, to realize a bearingless system with three degrees of freedom. The complete model represents the design of the experimental system to be realized in the laboratory.

Automotive diesel engine transient operation: modeling, optimization and control

Traditionally, the study of internal combustion engines operation has focused on the steady-state performance. However, the daily driving schedule of automotive engines is inherently related to unsteady conditions. There are various operating conditions experienced by (diesel) engines that can be classified as transient. Besides the variation of the engine operating point, in terms of engine speed and torque, also the warm up phase can be considered as a transient condition. Chapter 2 has to do with this thermal transient condition; more precisely the main issue is the performance of a Selective Catalytic Reduction (SCR) system during cold start and warm up phases of the engine. The proposal of the underlying work is to investigate and identify optimal exhaust line heating strategies, to provide a fast activation of the catalytic reactions on SCR. Chapters 3 and 4 focus the attention on the dynamic behavior of the engine, when considering typical driving conditions. The common approach to dynamic optimization involves the solution of a single optimal-control problem. However, this approach requires the availability of models that are valid throughout the whole engine operating range and actuator ranges. In addition, the result of the optimization is meaningful only if the model is very accurate. Chapter 3 proposes a methodology to circumvent those demanding requirements: an iteration between transient measurements to refine a purpose-built model and a dynamic optimization which is constrained to the model validity region. Moreover all numerical methods required to implement this procedure are presented. Chapter 4 proposes an approach to derive a transient feedforward control system in an automated way. It relies on optimal control theory to solve a dynamic optimization problem for fast transients. From the optimal solutions, the relevant information is extracted and stored in maps spanned by the engine speed and the torque gradient.

Development of a microgrid with renewable energy sources and electrochemical storage system integration

Beside the traditional paradigm of "centralized" power generation, a new concept of "distributed" generation is emerging, in which the same user becomes pro-sumer. During this transition, the Energy Storage Systems (ESS) can provide multiple services and features, which are necessary for a higher quality of the electrical system and for the optimization of non-programmable Renewable Energy Source (RES) power plants. A ESS prototype was designed, developed and integrated into a renewable energy production system in order to create a smart microgrid and consequently manage in an efficient and intelligent way the energy flow as a function of the power demand. The produced energy can be introduced into the grid, supplied to the load directly or stored in batteries. The microgrid is composed by a 7 kW wind turbine (WT) and a 17 kW photovoltaic (PV) plant are part of. The load is given by electrical utilities of a cheese factory. The ESS is composed by the following two subsystems, a Battery Energy Storage System (BESS) and a Power Control System (PCS). With the aim of sizing the ESS, a Remote Grid Analyzer (RGA) was designed, realized and connected to the wind turbine, photovoltaic plant and the switchboard. Afterwards, different electrochemical storage technologies were studied, and taking into account the load requirements present in the cheese factory, the most suitable solution was identified in the high temperatures salt Na-NiCl2 battery technology. The data acquisition from all electrical utilities provided a detailed load analysis, indicating the optimal storage size equal to a 30 kW battery system. Moreover a container was designed and realized to locate the BESS and PCS, meeting all the requirements and safety conditions. Furthermore, a smart control system was implemented in order to handle the different applications of the ESS, such as peak shaving or load levelling.

NLGA based Active Control in Aerospace: fault tolerability, disturbance rejection, and parameter estimation

A new control scheme has been presented in this thesis. Based on the NonLinear Geometric Approach, the proposed Active Control System represents a new way to see the reconfigurable controllers for aerospace applications. The presence of the Diagnosis module (providing the estimation of generic signals which, based on the case, can be faults, disturbances or system parameters), mean feature of the depicted Active Control System, is a characteristic shared by three well known control systems: the Active Fault Tolerant Controls, the Indirect Adaptive Controls and the Active Disturbance Rejection Controls. The standard NonLinear Geometric Approach (NLGA) has been accurately investigated and than improved to extend its applicability to more complex models. The standard NLGA procedure has been modified to take account of feasible and estimable sets of unknown signals. Furthermore the application of the Singular Perturbations approximation has led to the solution of Detection and Isolation problems in scenarios too complex to be solved by the standard NLGA. Also the estimation process has been improved, where multiple redundant measuremtent are available, by the introduction of a new algorithm, here called "Least Squares - Sliding Mode". It guarantees optimality, in the sense of the least squares, and finite estimation time, in the sense of the sliding mode. The Active Control System concept has been formalized in two controller: a nonlinear backstepping controller and a nonlinear composite controller. Particularly interesting is the integration, in the controller design, of the estimations coming from the Diagnosis module. Stability proofs are provided for both the control schemes. Finally, different applications in aerospace have been provided to show the applicability and the effectiveness of the proposed NLGA-based Active Control System.