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

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

Vibration-Based structural health monitoring using piezoelectric transducers and parametric t-SNE

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.Peer ReviewedPostprint (published version

Structural dynamics branch research and accomplishments to FY 1992

This publication contains a collection of fiscal year 1992 research highlights from the Structural Dynamics Branch at NASA LeRC. Highlights from the branch's major work areas--Aeroelasticity, Vibration Control, Dynamic Systems, and Computational Structural Methods are included in the report as well as a listing of the fiscal year 1992 branch publications

Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches $\mathbf{O}(10^6)$ servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in $\approx 500 ps$ and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where $3\times$ less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in $\mathbf{O}(10^{-3}) s$. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved $>20\%$ with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

Study and Development of Mechatronic Devices and Machine Learning Schemes for Industrial Applications

Obiettivo del presente progetto di dottorato è lo studio e sviluppo di sistemi meccatronici e di modelli machine learning per macchine operatrici e celle robotizzate al fine di incrementarne le prestazioni operative e gestionali. Le pressanti esigenze del mercato hanno imposto lavorazioni con livelli di accuratezza sempre più elevati, tempi di risposta e di produzione ridotti e a costi contenuti. In questo contesto nasce il progetto di dottorato, focalizzato su applicazioni di lavorazioni meccaniche (e.g. fresatura), che includono sistemi complessi quali, ad esempio, macchine a 5 assi e, tipicamente, robot industriali, il cui utilizzo varia a seconda dell’impiego. Oltre alle specifiche problematiche delle lavorazioni, si deve anche considerare l’interazione macchina-robot per permettere un’efficiente capacità e gestione dell’intero impianto. La complessità di questo scenario può evidenziare sia specifiche problematiche inerenti alle lavorazioni (e.g. vibrazioni) sia inefficienze più generali che riguardano l’impianto produttivo (e.g. asservimento delle macchine con robot, consumo energetico). Vista la vastità della tematica, il progetto si è suddiviso in due parti, lo studio e sviluppo di due specifici dispositivi meccatronici, basati sull’impiego di attuatori piezoelettrici, che puntano principalmente alla compensazione di vibrazioni indotte dal processo di lavorazione, e l’integrazione di robot per l’asservimento di macchine utensili in celle robotizzate, impiegando modelli di machine learning per definire le traiettorie ed i punti di raggiungibilità del robot, al fine di migliorarne l’accuratezza del posizionamento del pezzo in diverse condizioni. In conclusione, la presente tesi vuole proporre soluzioni meccatroniche e di machine learning per incrementare le prestazioni di macchine e sistemi robotizzati convenzionali. I sistemi studiati possono essere integrati in celle robotizzate, focalizzandosi sia su problematiche specifiche delle lavorazioni in macchine operatrici sia su problematiche a livello di impianto robot-macchina. Le ricerche hanno riguardato un’approfondita valutazione dello stato dell’arte, la definizione dei modelli teorici, la progettazione funzionale e l’identificazione delle criticità del design dei prototipi, la realizzazione delle simulazioni e delle prove sperimentali e l’analisi dei risultati.The aim of this Ph.D. project is the study and development of mechatronic systems and machine learning models for machine tools and robotic applications to improve their performances. The industrial demands have imposed an ever-increasing accuracy and efficiency requirement whilst constraining the cost. In this context, this project focuses on machining processes (e.g. milling) that include complex systems such as 5-axes machine tool and industrial robots, employed for various applications. Beside the issues related to the machining process itself, the interaction between the machining centre and the robot must be considered for the complete industrial plant’s improvement. This scenario´s complexity depicts both specific machining problematics (e.g. vibrations) and more general issues related to the complete plant, such as machine tending with an industrial robot and energy consumption. Regarding the immensity of this area, this project is divided in two parts, the study and development of two mechatronic devices, based on piezoelectric stack actuators, for the active vibration control during the machining process, and the robot machine tending within the robotic cell, employing machine learning schemes for the trajectory definition and robot reachability to improve the corresponding positioning accuracy. In conclusion, this thesis aims to provide a set of solutions, based on mechatronic devices and machine learning schemes, to improve the conventional machining centre and the robotic systems performances. The studied systems can be integrated within a robotic cell, focusing on issues related to the specific machining process and to the interaction between robot-machining centre. This research required a thorough study of the state-of-the-art, the formulation of theoretical models, the functional design development, the identification of the critical aspects in the prototype designs, the simulation and experimental campaigns, and the analysis of the obtained results

Plasma sprayed titanium coatings with/without a shroud

Abstract: Titanium coatings were deposited by plasma spraying with and without a shroud. The titanium coatings were then assessed by scanning electron microscopy. A comparison in microstructure between titanium coatings with and without the shroud was carried out. The results showed that the shroud played an important role in protecting the titanium particles from oxidation. The presence of the shroud led to a reduction in coating porosity. The reduction in air entrainment with t he shroud resulted in better heating of the particles, and an enhanced microstructure with lower porosity in the shrouded titanium coatings were observed compared to the air plasma sprayed counterpart