Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Control of large-scale structures with large uncertainties

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 279-300).Performance-based design is a design approach that satisfies motion constraints as its primary goal, and then verifies for strength. The approach is traditionally executed by appropriately sizing stiffnesses, but recently, passive energy dissipation systems have gained popularity. Semi-active and active energy dissipation systems have been shown to outperform purely passive systems, but they are not yet widely accepted in the construction and structural engineering fields. Several factors are impeding the application of semi-active and active damping systems, such as large modeling uncertainties that are inherent to large-scale structures, limited state measurements, lack of mechanically reliable control devices, large power requirements, and the need for robust controllers. In order to enhance acceptability of feedback control systems to civil structures, an integrated control strategy designed for large-scale structures with large parametric uncertainties is proposed. The control strategy comprises a novel controller, as well as a new semi-active mechanical damping device. Specifically, the controller is an adaptive black-box representation that creates and optimizes control laws sequentially during an excitation, with no prior training. The novel feature is its online organization of the input space. The representation only requires limited observations for constructing an efficient representation, which allows control of unknown systems with limited state measurements. The semi-active mechanical device consists of a friction device inspired by a vehicle drum brakes, with a viscous and a stiffness element installed in parallel. Its unique characteristic is its theoretical damping force reaching the order of 100 kN, using a friction mechanism powered with a single 12-volts battery. It is conceived using mechanically reliable technologies, which is a solution to large power requirement and mechanical robustness. The integrated control system is simulated on an existing structure located in Boston, MA, as a replacement to the existing viscous damping system. Simulation results show that the integrated control system can mitigate wind vibrations as well as the current damping strategy, utilizing only one third of devices. In addition, the system created effective control rules for several types of earthquake excitations with no prior training, performing similarly to an optimal controller using full parametric and state knowledge.by Simon Laflamme.Ph.D

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

Development of new learning control approaches

Ph.DDOCTOR OF PHILOSOPH

Safe Haptics-enabled Patient-Robot Interaction for Robotic and Telerobotic Rehabilitation of Neuromuscular Disorders: Control Design and Analysis

Motivation: Current statistics show that the population of seniors and the incidence rate of age-related neuromuscular disorders are rapidly increasing worldwide. Improving medical care is likely to increase the survival rate but will result in even more patients in need of Assistive, Rehabilitation and Assessment (ARA) services for extended periods which will place a significant burden on the world\u27s healthcare systems. In many cases, the only alternative is limited and often delayed outpatient therapy. The situation will be worse for patients in remote areas. One potential solution is to develop technologies that provide efficient and safe means of in-hospital and in-home kinesthetic rehabilitation. In this regard, Haptics-enabled Interactive Robotic Neurorehabilitation (HIRN) systems have been developed. Existing Challenges: Although there are specific advantages with the use of HIRN technologies, there still exist several technical and control challenges, e.g., (a) absence of direct interactive physical interaction between therapists and patients; (b) questionable adaptability and flexibility considering the sensorimotor needs of patients; (c) limited accessibility in remote areas; and (d) guaranteeing patient-robot interaction safety while maximizing system transparency, especially when high control effort is needed for severely disabled patients, when the robot is to be used in a patient\u27s home or when the patient experiences involuntary movements. These challenges have provided the motivation for this research. Research Statement: In this project, a novel haptics-enabled telerobotic rehabilitation framework is designed, analyzed and implemented that can be used as a new paradigm for delivering motor therapy which gives therapists direct kinesthetic supervision over the robotic rehabilitation procedure. The system also allows for kinesthetic remote and ultimately in-home rehabilitation. To guarantee interaction safety while maximizing the performance of the system, a new framework for designing stabilizing controllers is developed initially based on small-gain theory and then completed using strong passivity theory. The proposed control framework takes into account knowledge about the variable biomechanical capabilities of the patient\u27s limb(s) in absorbing interaction forces and mechanical energy. The technique is generalized for use for classical rehabilitation robotic systems to realize patient-robot interaction safety while enhancing performance. In the next step, the proposed telerobotic system is studied as a modality of training for classical HIRN systems. The goal is to first model and then regenerate the prescribed kinesthetic supervision of an expert therapist. To broaden the population of patients who can use the technology and HIRN systems, a new control strategy is designed for patients experiencing involuntary movements. As the last step, the outcomes of the proposed theoretical and technological developments are translated to designing assistive mechatronic tools for patients with force and motion control deficits. This study shows that proper augmentation of haptic inputs can not only enhance the transparency and safety of robotic and telerobotic rehabilitation systems, but it can also assist patients with force and motion control deficiencies

Inclusive Intelligent Learning Management System Framework - Application of Data Science in Inclusive Education

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBeing a disabled student the author faced higher education with a handicap which as experience studying during COVID 19 confinement periods matched the findings in recent research about the importance of digital accessibility through more e-learning intensive academic experiences. Narrative and systematic literature reviews enabled providing context in World Health Organization’s International Classification of Functioning, Disability and Health, legal and standards framework and information technology and communication state-of-the art. Assessing Portuguese higher education institutions’ web sites alerted to the fact that only outlying institutions implemented near perfect, accessibility-wise, websites. Therefore a gap was identified in how accessible the Portuguese higher education websites are, the needs of all students, including those with disabilities, and even the accessibility minimum legal requirements for digital products and the services provided by public or publicly funded organizations. Having identified a problem in society and exploring the scientific base of knowledge for context and state of the art was a first stage in the Design Science Research methodology, to which followed development and validation cycles of an Inclusive Intelligent Learning Management System Framework. The framework blends various Data Science study fields contributions with accessibility guidelines compliant interface design and content upload accessibility compliance assessment. Validation was provided by a focus group whose inputs were considered for the version presented in this dissertation. Not being the purpose of the research to deliver a complete implementation of the framework and lacking consistent data to put all the modules interacting with each other, the most relevant modules were tested with open data as proof of concept. The rigor cycle of DSR started with the inclusion of the previous thesis on Atlântica University Institute Scientific Repository and is to be completed with the publication of this thesis and the already started PhD’s findings in relevant journals and conferences

Iterative learning control of crystallisation systems

Under the increasing pressure of issues like reducing the time to market, managing lower production costs, and improving the flexibility of operation, batch process industries thrive towards the production of high value added commodity, i.e. specialty chemicals, pharmaceuticals, agricultural, and biotechnology enabled products. For better design, consistent operation and improved control of batch chemical processes one cannot ignore the sensing and computational blessings provided by modern sensors, computers, algorithms, and software. In addition, there is a growing demand for modelling and control tools based on process operating data. This study is focused on developing process operation data-based iterative learning control (ILC) strategies for batch processes, more specifically for batch crystallisation systems. In order to proceed, the research took a step backward to explore the existing control strategies, fundamentals, mechanisms, and various process analytical technology (PAT) tools used in batch crystallisation control. From the basics of the background study, an operating data-driven ILC approach was developed to improve the product quality from batch-to-batch. The concept of ILC is to exploit the repetitive nature of batch processes to automate recipe updating using process knowledge obtained from previous runs. The methodology stated here was based on the linear time varying (LTV) perturbation model in an ILC framework to provide a convergent batch-to-batch improvement of the process performance indicator. In an attempt to create uniqueness in the research, a novel hierarchical ILC (HILC) scheme was proposed for the systematic design of the supersaturation control (SSC) of a seeded batch cooling crystalliser. This model free control approach is implemented in a hierarchical structure by assigning data-driven supersaturation controller on the upper level and a simple temperature controller in the lower level. In order to familiarise with other data based control of crystallisation processes, the study rehearsed the existing direct nucleation control (DNC) approach. However, this part was more committed to perform a detailed strategic investigation of different possible structures of DNC and to compare the results with that of a first principle model based optimisation for the very first time. The DNC results in fact outperformed the model based optimisation approach and established an ultimate guideline to select the preferable DNC structure. Batch chemical processes are distributed as well as nonlinear in nature which need to be operated over a wide range of operating conditions and often near the boundary of the admissible region. As the linear lumped model predictive controllers (MPCs) often subject to severe performance limitations, there is a growing demand of simple data driven nonlinear control strategy to control batch crystallisers that will consider the spatio-temporal aspects. In this study, an operating data-driven polynomial chaos expansion (PCE) based nonlinear surrogate modelling and optimisation strategy was presented for batch crystallisation processes. Model validation and optimisation results confirmed this approach as a promise to nonlinear control. The evaluations of the proposed data based methodologies were carried out by simulation case studies, laboratory experiments and industrial pilot plant experiments. For all the simulation case studies a detailed mathematical models covering reaction kinetics and heat mass balances were developed for a batch cooling crystallisation system of Paracetamol in water. Based on these models, rigorous simulation programs were developed in MATLAB®, which was then treated as the real batch cooling crystallisation system. The laboratory experimental works were carried out using a lab scale system of Paracetamol and iso-Propyl alcohol (IPA). All the experimental works including the qualitative and quantitative monitoring of the crystallisation experiments and products demonstrated an inclusive application of various in situ process analytical technology (PAT) tools, such as focused beam reflectance measurement (FBRM), UV/Vis spectroscopy and particle vision measurement (PVM) as well. The industrial pilot scale study was carried out in GlaxoSmithKline Bangladesh Limited, Bangladesh, and the system of experiments was Paracetamol and other powdered excipients used to make paracetamol tablets. The methodologies presented in this thesis provide a comprehensive framework for data-based dynamic optimisation and control of crystallisation processes. All the simulation and experimental evaluations of the proposed approaches emphasised the potential of the data-driven techniques to provide considerable advances in the current state-of-the-art in crystallisation control

Incremental Learning Through Unsupervised Adaptation in Video Face Recognition

Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo] Durante a última década, os métodos baseados en deep learning trouxeron un salto significativo no rendemento dos sistemas de visión artificial. Unha das claves neste éxito foi a creación de grandes conxuntos de datos perfectamente etiquetados para usar durante o adestramento. En certa forma, as redes de deep learning resumen esta enorme cantidade datos en prácticos vectores multidimensionais. Por este motivo, cando as diferenzas entre os datos de adestramento e os adquiridos durante o funcionamento dos sistemas (debido a factores como o contexto de adquisición) son especialmente notorias, as redes de deep learning son susceptibles de sufrir degradación no rendemento. Mentres que a solución inmediata a este tipo de problemas sería a de recorrer a unha recolección adicional de imaxes, co seu correspondente proceso de etiquetado, esta dista moito de ser óptima. A gran cantidade de posibles variacións que presenta o mundo visual converten rápido este enfoque nunha tarefa sen fin. Máis aínda cando existen aplicacións específicas nas que esta acción é difícil, ou incluso imposible, de realizar debido a problemas de custos ou de privacidade. Esta tese propón abordar todos estes problemas usando a perspectiva da adaptación. Así, a hipótese central consiste en asumir que é posible utilizar os datos non etiquetados adquiridos durante o funcionamento para mellorar o rendemento que obteríamos con sistemas de recoñecemento xerais. Para isto, e como proba de concepto, o campo de estudo da tese restrinxiuse ao recoñecemento de caras. Esta é unha aplicación paradigmática na cal o contexto de adquisición pode ser especialmente relevante. Este traballo comeza examinando as diferenzas intrínsecas entre algúns dos contextos específicos nos que se pode necesitar o recoñecemento de caras e como estas afectan ao rendemento. Desta maneira, comparamos distintas bases de datos (xunto cos seus contextos) entre elas, usando algúns dos descritores de características máis avanzados e así determinar a necesidade real de adaptación. A partir desta punto, pasamos a presentar o método novo, que representa a principal contribución da tese: o Dynamic Ensemble of SVM (De-SVM). Este método implementa a capacidade de adaptación utilizando unha aprendizaxe incremental non supervisada na que as súas propias predicións se usan como pseudo-etiquetas durante as actualizacións (a estratexia de auto-adestramento). Os experimentos realizáronse baixo condicións de vídeo-vixilancia, un exemplo paradigmático dun contexto moi específico no que os procesos de etiquetado son particularmente complicados. As ideas claves de De-SVM probáronse en diferentes sub-problemas de recoñecemento de caras: a verificación de caras e recoñecemento de caras en conxunto pechado e en conxunto aberto. Os resultados acadados mostran un comportamento prometedor en termos de adquisición de coñecemento sen supervisión así como robustez contra impostores. Ademais, este rendemento é capaz de superar a outros métodos do estado da arte que non posúen esta capacidade de adaptación.[Resumen] Durante la última década, los métodos basados en deep learning trajeron un salto significativo en el rendimiento de los sistemas de visión artificial. Una de las claves en este éxito fue la creación de grandes conjuntos de datos perfectamente etiquetados para usar durante el entrenamiento. En cierta forma, las redes de deep learning resumen esta enorme cantidad datos en prácticos vectores multidimensionales. Por este motivo, cuando las diferencias entre los datos de entrenamiento y los adquiridos durante el funcionamiento de los sistemas (debido a factores como el contexto de adquisición) son especialmente notorias, las redes de deep learning son susceptibles de sufrir degradación en el rendimiento. Mientras que la solución a este tipo de problemas es recurrir a una recolección adicional de imágenes, con su correspondiente proceso de etiquetado, esta dista mucho de ser óptima. La gran cantidad de posibles variaciones que presenta el mundo visual convierten rápido este enfoque en una tarea sin fin. Más aún cuando existen aplicaciones específicas en las que esta acción es difícil, o incluso imposible, de realizar; debido a problemas de costes o de privacidad. Esta tesis propone abordar todos estos problemas usando la perspectiva de la adaptación. Así, la hipótesis central consiste en asumir que es posible utilizar los datos no etiquetados adquiridos durante el funcionamiento para mejorar el rendimiento que se obtendría con sistemas de reconocimiento generales. Para esto, y como prueba de concepto, el campo de estudio de la tesis se restringió al reconocimiento de caras. Esta es una aplicación paradigmática en la cual el contexto de adquisición puede ser especialmente relevante. Este trabajo comienza examinando las diferencias entre algunos de los contextos específicos en los que se puede necesitar el reconocimiento de caras y así como sus efectos en términos de rendimiento. De esta manera, comparamos distintas ba ses de datos (y sus contextos) entre ellas, usando algunos de los descriptores de características más avanzados para así determinar la necesidad real de adaptación. A partir de este punto, pasamos a presentar el nuevo método, que representa la principal contribución de la tesis: el Dynamic Ensemble of SVM (De- SVM). Este método implementa la capacidad de adaptación utilizando un aprendizaje incremental no supervisado en la que sus propias predicciones se usan cómo pseudo-etiquetas durante las actualizaciones (la estrategia de auto-entrenamiento). Los experimentos se realizaron bajo condiciones de vídeo-vigilancia, un ejemplo paradigmático de contexto muy específico en el que los procesos de etiquetado son particularmente complicados. Las ideas claves de De- SVM se probaron en varios sub-problemas del reconocimiento de caras: la verificación de caras y reconocimiento de caras de conjunto cerrado y conjunto abierto. Los resultados muestran un comportamiento prometedor en términos de adquisición de conocimiento así como de robustez contra impostores. Además, este rendimiento es capaz de superar a otros métodos del estado del arte que no poseen esta capacidad de adaptación.[Abstract] In the last decade, deep learning has brought an unprecedented leap forward for computer vision general classification problems. One of the keys to this success is the availability of extensive and wealthy annotated datasets to use as training samples. In some sense, a deep learning network summarises this enormous amount of data into handy vector representations. For this reason, when the differences between training datasets and the data acquired during operation (due to factors such as the acquisition context) are highly marked, end-to-end deep learning methods are susceptible to suffer performance degradation. While the immediate solution to mitigate these problems is to resort to an additional data collection and its correspondent annotation procedure, this solution is far from optimal. The immeasurable possible variations of the visual world can convert the collection and annotation of data into an endless task. Even more when there are specific applications in which this additional action is difficult or simply not possible to perform due to, among other reasons, cost-related problems or privacy issues. This Thesis proposes to tackle all these problems from the adaptation point of view. Thus, the central hypothesis assumes that it is possible to use operational data with almost no supervision to improve the performance we would achieve with general-purpose recognition systems. To do so, and as a proof-of-concept, the field of study of this Thesis is restricted to face recognition, a paradigmatic application in which the context of acquisition can be especially relevant. This work begins by examining the intrinsic differences between some of the face recognition contexts and how they directly affect performance. To do it, we compare different datasets, and their contexts, against each other using some of the most advanced feature representations available to determine the actual need for adaptation. From this point, we move to present the novel method, representing the central contribution of the Thesis: the Dynamic Ensembles of SVM (De-SVM). This method implements the adaptation capabilities by performing unsupervised incremental learning using its own predictions as pseudo-labels for the update decision (the self-training strategy). Experiments are performed under video surveillance conditions, a paradigmatic example of a very specific context in which labelling processes are particularly complicated. The core ideas of De-SVM are tested in different face recognition sub-problems: face verification and, the more complex, general closed- and open-set face recognition. In terms of the achieved results, experiments have shown a promising behaviour in terms of both unsupervised knowledge acquisition and robustness against impostors, surpassing the performances achieved by state-of-the-art non-adaptive methods.Funding and Technical Resources For the successful development of this Thesis, it was necessary to rely on series of indispensable means included in the following list: • Working material, human and financial support primarily by the CITIC and the Computer Architecture Group of the University of A Coruña and CiTIUS of University of Santiago de Compostela, along with a PhD grant funded by Xunta the Galicia and the European Social Fund. • Access to bibliographical material through the library of the University of A Coruña. • Additional funding through the following research projects: State funding by the Ministry of Economy and Competitiveness of Spain (project TIN2017-90135-R MINECO, FEDER)

Transforming our World through Universal Design for Human Development

An environment, or any building product or service in it, should ideally be designed to meet the needs of all those who wish to use it. Universal Design is the design and composition of environments, products, and services so that they can be accessed, understood and used to the greatest extent possible by all people, regardless of their age, size, ability or disability. It creates products, services and environments that meet people’s needs. In short, Universal Design is good design. This book presents the proceedings of UD2022, the 6th International Conference on Universal Design, held from 7 - 9 September 2022 in Brescia, Italy.The conference is targeted at professionals and academics interested in the theme of universal design as related to the built environment and the wellbeing of users, but also covers mobility and urban environments, knowledge, and information transfer, bringing together research knowledge and best practice from all over the world. The book contains 72 papers from 13 countries, grouped into 8 sections and covering topics including the design of inclusive natural environments and urban spaces, communities, neighborhoods and cities; housing; healthcare; mobility and transport systems; and universally- designed learning environments, work places, cultural and recreational spaces. One section is devoted to universal design and cultural heritage, which had a particular focus at this edition of the conference. The book reflects the professional and disciplinary diversity represented in the UD movement, and will be of interest to all those whose work involves inclusive design