Optimal fuzzy iterative learning control based on artificial bee colony for vibration control of piezoelectric smart structures

Combining P-type iterative learning (IL) control, fuzzy logic control and artificial bee colony (ABC) algorithm, a new optimal fuzzy IL controller is designed for active vibration control of piezoelectric smart structures. In order to accelerate the learning speed of feedback gain, the fuzzy logic controller is integrated into the ANSYS finite element (FE) models by using APDL (ANSYS Parameter Design Language) approach to adjust adaptively the learning gain of P-type IL control. For improving the performance and robustness of the fuzzy logic controller as well as diminishing human intervention in the operation process, ABC algorithm is used to automatically identify the optimal configurations for values in fuzzy query table, fuzzification parameters and defuzzification parameters, and the main program of ABC algorithm is operated in MATLAB. The active vibration equations are driven from the FE equations for the dynamic response of a linear elastic piezoelectric smart structure. Considering the vibrations generated by various external disturbances, the optimal fuzzy IL controller is numerically investigated for a clamped piezoelectric smart plate. Results demonstrate that the proposed control approach makes the feedback gain has a fast learning speed and performs excellent in vibration suppression. This is demonstrated in the results by comparing the new control approach with the P-type IL control

Advanced Control of Piezoelectric Actuators.

168 p.A lo largo de las últimas décadas, la ingeniería de precisión ha tenido un papel importante como tecnología puntera donde la tendencia a la reducción de tamaño de las herramientas industriales ha sido clave. Los procesos industriales comenzaron a demandar precisión en el rango de nanómetros a micrómetros. Pese a que los actuadores convencionales no pueden reducirse lo suficiente ni lograr tal exactitud, los actuadores piezoeléctricos son una tecnología innovadora en este campo y su rendimiento aún está en estudio en la comunidad científica. Los actuadores piezoeléctricos se usan comúnmente en micro y nanomecatrónica para aplicaciones de posicionamiento debido a su alta resolución y fuerza de actuación (pueden llegar a soportar fuerzas de hasta 100 Newtons) en comparación con su tamaño. Todas estas características también se pueden combinar con una actuación rápida y rigidez, según los requisitos de la aplicación. Por lo tanto, con estas características, los actuadores piezoeléctricos pueden ser utilizados en una amplia variedad de aplicaciones industriales. Los efectos negativos, como la fluencia, vibraciones y la histéresis, se estudian comúnmente para mejorar el rendimiento cuando se requiere una alta precisión. Uno de los efectos que más reduce el rendimiento de los PEA es la histéresis. Esto se produce especialmente cuando el actuador está en una aplicación de guiado, por lo que la histéresis puede inducir errores que pueden alcanzar un valor de hasta 22%. Este fenómeno no lineal se puede definir como un efecto generado por la combinación de acciones mecánicas y eléctricas que depende de estados previos. La histéresis se puede reducir principalmente mediante dos estrategias: rediseño de materiales o algoritmos de control tipo feedback. El rediseño de material comprende varias desventajas por lo que el motivo principal de esta tesis está enfocado al diseño de algoritmos de control para reducir la histéresis. El objetivo principal de esta tesis es el desarrollo de estrategias de control avanzadas que puedan mejorar la precisión de seguimiento de los actuadores piezoeléctricos comerciale

Dynamics and Control of Smart Structures for Space Applications

Smart materials are one of the key emerging technologies for a variety of space systems ranging in their applications from instrumentation to structural design. The underlying principle of smart materials is that they are materials that can change their properties based on an input, typically a voltage or current. When these materials are incorporated into structures, they create smart structures. This work is concerned with the dynamics and control of three smart structures: a membrane structure with shape memory alloys for control of the membrane surface flatness, a flexible manipulator with a collocated piezoelectric sensor/actuator pair for active vibration control, and a piezoelectric nanopositioner for control of instrumentation. Shape memory alloys are used to control the surface flatness of a prototype membrane structure. As these actuators exhibit a hysteretic nonlinearity, they need their own controller to operate as required. The membrane structures surface flatness is then controlled by the shape memory alloys, and two techniques are developed: genetic algorithm and proportional-integral controllers. This would represent the removal of one of the main obstacles preventing the use of membrane structures in space for high precision applications, such as a C-band synthetic aperture radar antenna. Next, an adaptive positive position feedback law is developed for control of a structure with a collocated piezoelectric sensor/actuator pair, with unknown natural frequencies. This control law is then combined with the input shaping technique for slew maneuvers of a single-link flexible manipulator. As an alternative to the adaptive positive position feedback law, genetic algorithms are investigated as both system identification techniques and as a tool for optimal controller design in vibration suppression. These controllers are all verified through both simulation and experiments. The third area of investigation is on the nonlinear dynamics and control of piezoelectric actuators for nanopositioning applications. A state feedback integral plus double integral synchronization controller is designed to allow the piezoelectrics to form the basis of an ultra-precise 2-D Fabry-Perot interferometer as the gap spacing of the device could be controlled at the nanometer level. Next, an output feedback linear integral control law is examined explicitly for the piezoelectric actuators with its nonlinear behaviour modeled as an input nonlinearity to a linear system. Conditions for asymptotic stability are established and then the analysis is extended to the derivation of an output feedback integral synchronization controller that guarantees global asymptotic stability under input nonlinearities. Experiments are then performed to validate the analysis. In this work, the dynamics and control of these smart structures are addressed in the context of their three applications. The main objective of this work is to develop effective and reliable control strategies for smart structures that broaden their applicability to space systems

MECHANICAL ENERGY HARVESTER FOR POWERING RFID SYSTEMS COMPONENTS: MODELING, ANALYSIS, OPTIMIZATION AND DESIGN

Finding alternative power sources has been an important topic of study worldwide. It is vital to find substitutes for finite fossil fuels. Such substitutes may be termed renewable energy sources and infinite supplies. Such limitless sources are derived from ambient energy like wind energy, solar energy, sea waves energy; on the other hand, smart cities megaprojects have been receiving enormous amounts of funding to transition our lives into smart lives. Smart cities heavily rely on smart devices and electronics, which utilize small amounts of energy to run. Using batteries as the power source for such smart devices imposes environmental and labor cost issues. Moreover, in many cases, smart devices are in hard-to-access places, making accessibility for disposal and replacement difficult. Finally, battery waste harms the environment. To overcome these issues, vibration-based energy harvesters have been proposed and implemented. Vibration-based energy harvesters convert the dynamic or kinetic energy which is generated due to the motion of an object into electric energy. Energy transduction mechanisms can be delivered based on piezoelectric, electromagnetic, or electrostatic methods; the piezoelectric method is generally preferred to the other methods, particularly if the frequency fluctuations are considerable. In response, piezoelectric vibration-based energy harvesters (PVEHs), have been modeled and analyzed widely. However, there are two challenges with PVEH: the maximum amount of extractable voltage and the effective (operational) frequency bandwidth are often insufficient. In this dissertation, a new type of integrated multiple system comprised of a cantilever and spring-oscillator is proposed to improve and develop the performance of the energy harvester in terms of extractable voltage and effective frequency bandwidth. The new energy harvester model is proposed to supply sufficient energy to power low-power electronic devices like RFID components. Due to the temperature fluctuations, the thermal effect over the performance of the harvester is initially studied. To alter the resonance frequency of the harvester structure, a rotating element system is considered and analyzed. In the analytical-numerical analysis, Hamilton’s principle along with Galerkin’s decomposition approach are adopted to derive the governing equations of the harvester motion and corresponding electric circuit. It is observed that integration of the spring-oscillator subsystem alters the boundary condition of the cantilever and subsequently reforms the resulting characteristic equation into a more complicated nonlinear transcendental equation. To find the resonance frequencies, this equation is solved numerically in MATLAB. It is observed that the inertial effects of the oscillator rendered to the cantilever via the restoring force effects of the spring significantly alter vibrational features of the harvester. Finally, the voltage frequency response function is analytically and numerically derived in a closed-from expression. Variations in parameter values enable the designer to mutate resonance frequencies and mode shape functions as desired. This is particularly important, since the generated energy from a PVEH is significant only if the excitation frequency coming from an external source matches the resonance (natural) frequency of the harvester structure. In subsequent sections of this work, the oscillator mass and spring stiffness are considered as the design parameters to maximize the harvestable voltage and effective frequency bandwidth, respectively. For the optimization, a genetic algorithm is adopted to find the optimal values. Since the voltage frequency response function cannot be implemented in a computer algorithm script, a suitable function approximator (regressor) is designed using fuzzy logic and neural networks. The voltage function requires manual assistance to find the resonance frequency and cannot be done automatically using computer algorithms. Specifically, to apply the numerical root-solver, one needs to manually provide the solver with an initial guess. Such an estimation is accomplished using a plot of the characteristic equation along with human visual inference. Thus, the entire process cannot be automated. Moreover, the voltage function encompasses several coefficients making the process computationally expensive. Thus, training a supervised machine learning regressor is essential. The trained regressor using adaptive-neuro-fuzzy-inference-system (ANFIS) is utilized in the genetic optimization procedure. The optimization problem is implemented, first to find the maximum voltage and second to find the maximum widened effective frequency bandwidth, which yields the optimal oscillator mass value along with the optimal spring stiffness value. As there is often no control over the external excitation frequency, it is helpful to design an adaptive energy harvester. This means that, considering a specific given value of the excitation frequency, energy harvester system parameters (oscillator mass and spring stiffness) need to be adjusted so that the resulting natural (resonance) frequency of the system aligns with the given excitation frequency. To do so, the given excitation frequency value is considered as the input and the system parameters are assumed as outputs which are estimated via the neural network fuzzy logic regressor. Finally, an experimental setup is implemented for a simple pure cantilever energy harvester triggered by impact excitations. Unlike the theoretical section, the experimental excitation is considered to be an impact excitation, which is a random process. The rationale for this is that, in the real world, the external source is a random trigger. Harmonic base excitations used in the theoretical chapters are to assess the performance of the energy harvester per standard criteria. To evaluate the performance of a proposed energy harvester model, the input excitation type consists of harmonic base triggers. In summary, this dissertation discusses several case studies and addresses key issues in the design of optimized piezoelectric vibration-based energy harvesters (PVEHs). First, an advanced model of the integrated systems is presented with equation derivations. Second, the proposed model is decomposed and analyzed in terms of mechanical and electrical frequency response functions. To do so, analytic-numeric methods are adopted. Later, influential parameters of the integrated system are detected. Then the proposed model is optimized with respect to the two vital criteria of maximum amount of extractable voltage and widened effective (operational) frequency bandwidth. Corresponding design (influential) parameters are found using neural network fuzzy logic along with genetic optimization algorithms, i.e., a soft computing method. The accuracy of the trained integrated algorithms is verified using the analytical-numerical closed-form expression of the voltage function. Then, an adaptive piezoelectric vibration-based energy harvester (PVEH) is designed. This final design pertains to the cases where the excitation (driving) frequency is given and constant, so the desired goal is to match the natural frequency of the system with the given driving frequency. In this response, a regressor using neural network fuzzy logic is designed to find the proper design parameters. Finally, the experimental setup is implemented and tested to report the maximum voltage harvested in each test execution

Damage reduction of on-board structures using piezoelectric components and active modal control — Application to a printed circuit board

International audienceThe reliability of Printed Circuit Boards (PCB) is critical for on-board electronic systems, particularly when subjected to severe stress conditions. This paper presents an approach to reduce vibration damage in PCBs that can be extended to the majority of on-board structures subjected to damage. Vibration damage highly depends on mode shapes under large band excitations. A solution to reduce vibration from the most damaging modes is to use active modal control for targeting efficiently control energy on most damaging modes. Following this modal-damage strategy, the most damaging modes are determined using a damage analysis based on an initial detailed Finite Element Model (FEM) of the PCB. The control is then designed using only a few piezoelectric components located so as to be essentially effective on these modes. The location algorithm of these active components uses a second simplified FEM including the damage simulation results. Finally, a classical Linear Quadratic Gaussian algorithm is used to determine the modal controller-observer gains. The effectiveness of the proposed method for PCBs is then examined through experiments with different high level excitations. The proposed control is finally validated by a new damage analysis of the controlled PCB to estimate damage reduction. Based on results obtained on an actual PCB, the modal approach the optimal link between damage estimation, optimal placement of actuators ,optimal control, and minimization of control energy. Moreover, the predictions of damage reduction and of actuation energy are in good agreement with experimental results, what shows that the modal description of on-board smart structure, in particular PCB , is the key point in damage reduction with vibration active control. Manuscript Click here to view linked Reference

Process analytical technology in food biotechnology

Biotechnology is an area where precision and reproducibility are vital. This is due to the fact that products are often in form of food, pharmaceutical or cosmetic products and therefore very close to the human being. To avoid human error during the production or the evaluation of the quality of a product and to increase the optimal utilization of raw materials, a very high amount of automation is desired. Tools in the food and chemical industry that aim to reach this degree of higher automation are summarized in an initiative called Process Analytical Technology (PAT). Within the scope of the PAT, is to provide new measurement technologies for the purpose of closed loop control in biotechnological processes. These processes are the most demanding processes in regards of control issues due to their very often biological rate-determining component. Most important for an automation attempt is deep process knowledge, which can only be achieved via appropriate measurements. These measurements can either be carried out directly, measuring a crucial physical value, or if not accessible either due to the lack of technology or a complicated sample state, via a soft-sensor.Even after several years the ideal aim of the PAT initiative is not fully implemented in the industry and in many production processes. On the one hand a lot effort still needs to be put into the development of more general algorithms which are more easy to implement and especially more reliable. On the other hand, not all the available advances in this field are employed yet. The potential users seem to stick to approved methods and show certain reservations towards new technologies.Die Biotechnologie ist ein Wissenschaftsbereich, in dem hohe Genauigkeit und Wiederholbarkeit eine wichtige Rolle spielen. Dies ist der Tatsache geschuldet, dass die hergestellten Produkte sehr oft den Bereichen Nahrungsmitteln, Pharmazeutika oder Kosmetik angehöhren und daher besonders den Menschen beeinflussen. Um den menschlichen Fehler bei der Produktion zu vermeiden, die Qualität eines Produktes zu sichern und die optimale Verwertung der Rohmaterialen zu gewährleisten, wird ein besonders hohes Maß an Automation angestrebt. Die Werkzeuge, die in der Nahrungsmittel- und chemischen Industrie hierfür zum Einsatz kommen, werden in der Process Analytical Technology (PAT) Initiative zusammengefasst. Ziel der PAT ist die Entwicklung zuverlässiger neuer Methoden, um Prozesse zu beschreiben und eine automatische Regelungsstrategie zu realisieren. Biotechnologische Prozesse gehören hierbei zu den aufwändigsten Regelungsaufgaben, da in den meisten Fällen eine biologische Komponente der entscheidende Faktor ist. Entscheidend für eine erfolgreiche Regelungsstrategie ist ein hohes Maß an Prozessverständnis. Dieses kann entweder durch eine direkte Messung der entscheidenden physikalischen, chemischen oder biologischen Größen gewonnen werden oder durch einen SoftSensor. Zusammengefasst zeigt sich, dass das finale Ziel der PAT Initiative auch nach einigen Jahren des Propagierens weder komplett in der Industrie noch bei vielen Produktionsprozessen angekommen ist. Auf der einen Seite liegt dies mit Sicherheit an der Tatsache, dass noch viel Arbeit in die Generalisierung von Algorithmen gesteckt werden muss. Diese müsse einfacher zu implementieren und vor allem noch zuverlässiger in der Funktionsweise sein. Auf der anderen Seite wurden jedoch auch Algorithmen, Regelungsstrategien und eigne Ansätze für einen neuartigen Sensor sowie einen Soft-Sensors vorgestellt, die großes Potential zeigen. Nicht zuletzt müssen die möglichen Anwender neue Strategien einsetzen und Vorbehalte gegenüber unbekannten Technologien ablegen

Smart Sensor Monitoring in Machining of Difficult-to-cut Materials

The research activities presented in this thesis are focused on the development of smart sensor monitoring procedures applied to diverse machining processes with particular reference to the machining of difficult-to-cut materials. This work will describe the whole smart sensor monitoring procedure starting from the configuration of the multiple sensor monitoring system for each specific application and proceeding with the methodologies for sensor signal detection and analysis aimed at the extraction of signal features to feed to intelligent decision-making systems based on artificial neural networks. The final aim is to perform tool condition monitoring in advanced machining processes in terms of tool wear diagnosis and forecast, in the perspective of zero defect manufacturing and green technologies. The work has been addressed within the framework of the national MIUR PON research project CAPRI, acronym for “Carrello per atterraggio con attuazione intelligente” (Landing Gear with Intelligent Actuation), and the research project STEP FAR, acronym for “Sviluppo di materiali e Tecnologie Ecocompatibili, di Processi di Foratura, taglio e di Assemblaggio Robotizzato” (Development of eco-compatible materials and technologies for robotised drilling and assembly processes). Both projects are sponsored by DAC, the Campania Technological Aerospace District, and involve two aerospace industries, Magnaghi Aeronautica S.p.A. and Leonardo S.p.A., respectively. Due to the industrial framework in which the projects were developed and taking advantage of the support from the industrial partners, the project activities have been carried out with the aim to contribute to the scientific research in the field of machining process monitoring as well as to promote the industrial applicability of the results. The thesis was structured in order to illustrate all the methodologies, the experimental tests and the results obtained from the research activities. It begins with an introduction to “Sensor monitoring of machining processes” (Chapter 2) with particular attention to the main sensor monitoring applications and the types of sensors which are employed in machining. The key methods for advanced sensor signal processing, including the implementation of sensor fusion technology, are discussed in details as they represent the basic input for cognitive decision-making systems construction. The chapter finally presents a brief discussion on cloud-based manufacturing which will represent one of the future developments of this research work. Chapters 3 and 4 illustrate the case studies of machining process sensor monitoring investigated in the research work. Within the CAPRI project, the feasibility of the dry turning process of Ti6Al4V alloy (Chapter 3) was studied with particular attention to the optimization of the machining parameters avoiding the use of coolant fluids. Since very rapid tool wear is experienced during dry machining of Titanium alloys, the multiple sensor monitoring system was used in order to develop a methodology based on a smart system for on line tool wear detection in terms of maximum flank wear land. Within the STEP FAR project, the drilling process of carbon fibre reinforced (CFRP) composite materials was studied using diverse experimental set-ups. Regarding the tools, three different types of drill bit were employed, including traditional as well as innovative geometry ones. Concerning the investigated materials, two different types of stack configurations were employed, namely CFRP/CFRP stacks and hybrid Al/CFRP stacks. Consequently, the machining parameters for each experimental campaign were varied, and also the methods for signal analysis were changed to verify the performance of the different methodologies. Finally, for each case different neural network configurations were investigated for cognitive-based decision making. First of all, the applicability of the system was tested in order to perform tool wear diagnosis and forecast. Then, the discussion proceeds with a further aim of the research work, which is the reduction of the number of selected sensor signal features, in order to improve the performance of the cognitive decision-making system, simplify modelling and facilitate the implementation of these methodologies in a cloud manufacturing approach to tool condition monitoring. Sensor fusion methodologies were applied to the extracted and selected sensor signal features in the perspective of feature reduction with the purpose to implement these procedures for big data analytics within the Industry 4.0 framework. In conclusion, the positive impact of the proposed tool condition monitoring methodologies based on multiple sensor signal acquisition and processing is illustrated, with particular reference to the reliable assessment of tool state in order to avoid too early or too late cutting tool substitution that negatively affect machining time and cost

Performance improvement of professional printing systems : from theory to practice

Performance Improvement of Professional Printing Systems: from theory to practice Markets demand continuously for higher quality, higher speed, and more energy-efficient professional printers. In this thesis, control strategies have been developed to improve the performance of both professional inkjet and laser printers. Drop-on-Demand (DoD) inkjet printing is considered as one of the most promising printing technologies. It offers several advantages including high speed, quiet operation, and compatibility with a variety of printing media. Nowadays, it has been used as low-cost and efficient manufacturing technology in a wide variety of markets. Although the performance requirements, which are imposed by the current applications, are tight, the future performance requirements are expected to be even more challenging. Several requirements are related to the jetted drop properties, namely, drop velocity, drop volume, drop velocity consistency, productivity, and reliability. Meeting the performance requirements is restricted by several operational issues that are associated with the design and operation of inkjet printheads. Major issues that are usually encountered are residual vibrations in and crosstalk among ink channels. This results in a poor printing quality for high-speed printing. Given any arbitrary bitmap, the main objective is to design actuation pulses such that variations in the velocity and volume of the jetted drops are minimized. Several model-based feedfoward control techniques using an existing model are implemented to generate appropriate input pulses for the printhead. Although the implementation of the model-based techniques shows a considerable improvement of the printhead performance compared with the current performance, further improvements are still necessary. We observe that besides the pulse shape the state of the ink surface at the nozzle plate (speed, position) at the start of the pulse influences the drop velocity considerably. This state at firing depends also on previous pixels in the bitmap of the image. Consequently, any pulse design has to guarantee almost the same initial state when firing a drop. Based on these facts, a model-free optimization scheme is developed to minimize the drop velocity variations taking into account the bitmap information. Experimental results show the effectiveness of the optimized pulses. Laser printing systems are highly depending on the appropriate combination of several design factors so as to become functional in a desired working range. The physical printing process involves multiple temperature set points at different places, precise electro-magnetic conditions, transfer of toner through certain pressures and layouts, and many other technical considerations. In the laser printing system there are several challenging issues and unknown disturbances. They originate from different sources, such as the printer itself (unknown phenomena appear, disturbances that are not foreseen, wear, contamination, failures, bugs), the environment of the system (power supply variations, temperature, humidity, vibrations), and the printing media (weight, coating, thermal properties, humidity characteristics, and initial temperature). These issues have a negative effect on the stability and performance of the laser printing system. The objective is to design a control scheme to achieve printing quality requirements and a high productivity. Good printing quality means that the fusing temperature should track a certain reference signal at different operating conditions. Based on the printing system behavior, we propose two different control schemes to cope with the large parameter variations and disturbances, namely, a Model Reference Adaptive Controller (MRAC) and a nonlinear (scheduled) observer-based output feedback control scheme. Both control techniques yield considerable performance improvements compared with the present industrial controller

High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks

Piezoelectric actuators (PEA) are frequently employed in applications where nano-Micr-odisplacement is required because of their high-precision performance. However, the positioning is affected substantially by the hysteresis which resembles in an nonlinear effect. In addition, hysteresis mathematical models own deficiencies that can influence on the reference following performance. The objective of this study was to enhance the tracking accuracy of a commercial PEA stack actuator with the implementation of a novel approach which consists in the use of a Super-Twisting Algorithm (STA) combined with artificial neural networks (ANN). A Lyapunov stability proof is bestowed to explain the theoretical solution. Experimental results of the proposed method were compared with a proportional-integral-derivative (PID) controller. The outcomes in a real PEA reported that the novel structure is stable as it was proved theoretically, and the experiments provided a significant error reduction in contrast with the PID.This research was funded by Basque Government and UPV/EHU projects