Soft robots present the opportunity to extend the capabilities currently demonstrated within the field of robotics. By utilizing primarily soft materials in their construction, soft robots are inherently safe to operate around humans, can handle delicate tasks without advanced controls, and are robust to shocks and impacts during deployment. While proof-of-concept devices have been demonstrated successfully, there remains a need for widely applicable, reliable soft robotic components. This dissertation presents sensors to reliably measure the large deformations exhibited in soft robotic structures and responsive structures enabled by variable stiffness materials that can switch from flexible to stiff on-demand. By characterizing the sensors from the material level, through the manufacturing, to the completed functional device, the fabrication processes can be depended upon to produce sensors with predictable, reliable performance. The sensors were applied to various soft robotic systems through implementation on the surface of the structures to measure surface strains, and embedded in the body of the robot to measure body deformations. The sensory feedback was used to reconstruct the state of and to perform closed-loop control of the soft robot's position. Variable stiffness materials that switch from rigid to soft through application of heat were leveraged to create responsive structures that can be deformed or reconfigured on-demand. This capability is necessary for soft robots to exert load onto the external environment and enables a wider range of interactions with target objects. The work presented in this dissertation furthers the field of soft robotics by illustrating a path toward proven, reliable soft sensors for measuring large strains and variable stiffness materials to create responsive structures.

Soft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies. First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions. Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC. Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.

This thesis describes the concept of programmable materials with tunable physical properties applicable to soft robots. We present these materials for three major applications in soft robotics: sensing, actuation, and robotic manipulation. The strain sensors recognize the internal stimuli in a soft robot, whereas the conductors collect the sensors’ signals to the control part. In the first part, we want to develop both stretchable strain sensors and conductors from a single material by programming a nanowire network’s electrical property, which we achieve through Electrical Welding (e-welding). We demonstrate the transformation of a Silver Nanowire (AgNW)-polymer sponge from a strain sensor to a stretchable conductor through e-welding. Using this method, we produced a soft hybrid e-skin having both a sensor and conductor from a single material. In the second part, we propose new active actuation solutions by obtaining quick, tunable pressure inside a soft material that we achieve through a liquid-gas phase transition of a stored liquid using an efficient electrode. We discuss the significant design variables to improve the performance and propose a new design for the electrodes, for enhancing actuation speed. We propose using low voltage equipment to trigger the phase transition to produce compact actuation technology for portable applications. Using this method, we produced a portable soft gripper. In the third and last part, we want to develop a simple robotic manipulation technology using a single-chambered soft body instead of a multi-chambered system. We propose using on-demand stiffness change in soft material to control the shape change of a single-chambered soft body. For this, we introduce a new concept of a stiffness tunable hybrid fiber: a fiber with stiff and soft parts connected in a series. We demonstrate a substantial change in membrane stiffness in the fiber through locking/unlocking of the soft part of the fiber. We integrated these fibers into a pneumatically operated single-chambered soft body to control its stiffness for on-demand shape change. If applied together, these three concepts could result in a fully printable, cheap, light, and easily controllable new generation soft robots with augmented functionalities.

Soft sensors have been suggested as potent tools for on-line estimations of critical bioprocess variables to be able to control the biological process in an as high extent as possible. The formation of inhibitory by-products in the form of organic acids, caused by an overflow of glucose, is a problem in most bioprocesses expressing recombinant proteins.    In this project a new method of controlling the glucose feeding in an Escherichia coli fed-batch cultivation expressing the green fluorescent protein (GFP) was investigated. The new controller system implemented in the software controlled the feed rate based on on-line HPLC measurements of the concentration of organic acids.      The results showed that the controller managed to down-regulate the inhibitory organic acids to a low level as it tried to keep the glucose uptake rate at an optimum for maximum cell growth. The results suggested that the controller could be a powerful tool to create a more secure reproducibility and to generate high product yields in recombinant protein productions.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-03-12T16:25:13Z No. of bitstreams: 1 ARIOSTON ARAÚJO DE MORAIS JÚNIOR - TESE PPGEQ 2015..pdf: 4075446 bytes, checksum: 58c7745e3f0cf289b5f5d0a0a774ed88 (MD5) / Made available in DSpace on 2018-03-12T16:25:13Z (GMT). No. of bitstreams: 1 ARIOSTON ARAÚJO DE MORAIS JÚNIOR - TESE PPGEQ 2015..pdf: 4075446 bytes, checksum: 58c7745e3f0cf289b5f5d0a0a774ed88 (MD5) Previous issue date: 2015-11-06 / Neste trabalho é proposta uma metodologia para construção de sensores virtuais implementados em software, com objetivo de estimar e prever o comportamento de impurezas na corrente de base de uma coluna de destilação de alta pureza, do processo produtivo do 1,2 Dicloroetano (C2H4Cl2). A aquisição dos dados utilizados na construção dos sensores virtuais foi realizada através do modelo matemático do processo, simulado com dados reais de uma planta industrial. O estudo específico engloba a modelagem matemática/termodinâmica e avaliação do comportamento estacionário e dinâmico dessa torre, simulada aqui no software Aspen Plus e DynamicsTM. Desse modo, o modelo fornece os dados necessários para inferência das impurezas relacionadas, que são os teores dos compostos tetracloreto de carbono (CCl4) e clorofórmio (CHCl3), ambos devem ser mantidos, respectivamente, em valores ≤3000 e ≤400 ppm (partes por milhão). A metodologia também aborda dois algoritmos de seleção de variáveis secundárias, que utilizam técnicas estatísticas multivariadas (algoritmo de todas as regressões possíveis-TRP e da análise de componentes principais-PCA). Verifica-se também nos dados gerados quanto a real ou não necessidade de remoção de erros grosseiros (outliers), por isso é também inserida na metodologia uma etapa de pré-processamento de dados. Foram selecionados os dez melhores modelos de inferência para cada uma das saídas. Diante dessa informação, os melhores modelos produzidos não utilizavam as concentrações dos compostos das correntes de alimentação e sim medições de temperaturas ao longo da torre. Uma importante conclusão do ponto de vista de construção de sensores virtuais, porque na maioria dos trabalhos desenvolvidos essas variáveis são cruciais na produção de bons resultados. O treinamento dos sensores virtuais foi efetuado em um ambiente ruidoso, haja vista que foram simulados ruídos inerentes às medições (ruídos brancos Gaussianos). Na etapa final, os sensores virtuais são construídos utilizando uma técnica de modelagem empírica, redes neurais artificiais (RNA), onde foram utilizadas RNA do tipo Perceptron Multicamadas (MLP). Foram também avaliadas diversas variações quanto ao número de neurônios e camadas ocultas das RNA, empregando como critério de parada a técnica de validação cruzada. Os sensores virtuais desenvolvidos apresentaram erros satisfatórios do ponto de vista de engenharia, uma boa análise de regressão e um bom erro médio quadrático. Logo, com essas estimativas espera-se a minimização e a previsão do comportamento transiente dos compostos no referido processo. / This work proposes a methodology for software implementation to make soft sensors. The goal is to estimate and predict the behavior of impurities in the bottom current of a highpurity distillation column, for 1,2-Dichloroethane or 1,2-DCE (C2H4Cl2) production. The data acquisition used in the construction of soft sensors was performed through a mathematical simulation of the process, with real industrial data taken from an industrial plant. A specific study involves the mathematical modeling, thermodynamics, evaluation of the steady state and the dynamic behavior of this process, simulated here in Aspen Plus and Aspen DynamicsTM software. Thus, the model provides the necessary data to infer the contents of the carbon impurities mentioned above, Tetrachloride (CCl4) and Chloroform (CHCl3), which are to be fixed approximately below 3000 and 400 ppm (parts per million) respectively. The methodology also covers selection algorithms of secondary variables, using multivariate statistical techniques: All Possible Regressions (TRP) and Principal Component Analysis (PCA). The data generated was checked in order to know whether to include or not a step for removal of outliers, so it was also included in the methodology one preprocessing data step. The ten best inference models were selected for each output concentration. With this information, these models do not use concentrations measurements in the feed streams but measurements of the temperature along the column. This is an important conclusion from the point of view of virtual sensors building, because in most of the literature reported these variables are crucial in getting good results. The training of soft sensors was done in a noisy environment, considering that simulated noise was inherent to measurements (Gaussian noise). In the final step, the soft sensors devices are constructed using an empirical modeling technique of artificial neural networks (ANN), which were generated ANN type Multilayer Perceptron (MLP). Several variations were also evaluated on the number of neurons and hidden layers of networks, employing as a stopping criterion the cross-validation technique. The developed soft sensors presented satisfactory errors from the engineering viewpoint, a good regression and a good mean square error. Finally, with these estimations it is expected to minimize and predict the transient behavior of the compounds in the referred process.

Although in many of the tasks in robotics, what is sought mainly includes accuracy, precision, flexibility, adaptivity, etc., yet in wearable robotics, there are some other aspects as well that could distinguish a reliable and promising approach. The three key elements that are addressed are as follows: control, actuation, and sensors. Where the goal for each of the previously mentioned objectives is to find a solution/design compatible with humans. A possible way to understand the human motor behaviours is to generate them on human-like robots. Biologically inspired action generation is promising in control of wearable robots as they provide more natural movements. Furthermore, wearable robotics shows exciting progress, also with its design. Soft exosuits use soft materials to build both sensors and actuators. This work investigates an adaptive representation model for actions in robotics. The concrete action model is composed of four modularities: pattern selection, spatial coordination, temporal coordination, and sensory-motor adaptation. Modularity in motor control might provide us with more insights about action learning and generalisation not only for humanoid robots but also for their biological counterparts. Successfully, we tested the model on a humanoid robot by learning to perform a variety of tasks (push recovery, walking, drawing, grasping, etc.). In the next part, we suggest several soft actuation mechanisms that overcome the problem of holding heavy loads and also the issue of on-line programming of the robot motion. The soft actuators use textile materials hosting thermoplastic polyurethane formed as inflatable tubes. Tubes were folded inside housing channels with one strain-limited side to create a flexor actuator. We proposed a new design to control the strained side of the actuator by adding four textile cords along its longitudinal axis. As a result, the actuator behaviour can be on-line programmed to bend and twist in several directions. In the last part of this thesis, we organised piezoresistive elements in a superimposition structure. The sensory structure is used on a sensory gripper to sense and distinguish between pressure and curvature stimuli. Next, we elaborated the sensing gripper by adding proximity sensing through conductive textile parts added to the gripper and work as capacitive sensors. We finally developed a versatile soft strain sensor that uses silicone tubes with an embedded solution that has an electrical resistance proportional to the strain applied on the tubes. Therefore, an entirely soft sensing glove exhibits hand gestures recognition. The proposed combinations of soft actuators, soft sensors, and biologically inspired action representation might open a new perspective to obtain smart wearable robots. / Obwohl bei vielen Aufgaben in der Robotik vor allem Genauigkeit, Präzision, Flexibilität, Anpassungsfähigkeit usw. gefragt sind, gibt es in der Wearable-Robotik auch einige andere Aspekte, die einen zuverlässigen und vielversprechenden Ansatz kennzeichnen. Die drei Schlüsselelemente, sind die folgenden: Steuerung, Aktuatoren und Sensoren. Dabei ist das Ziel für jedes der genannten Elemente, eine menschengerechte Lösung und ein menschengerechtes Design zu finden. Eine Möglichkeit, die menschliche Motorik zu verstehen, besteht darin, sie auf menschenähnlichen Robotern zu erzeugen. Biologisch inspirierte Bewegungsabläufe sind vielversprechend bei der Steuerung von tragbaren Robotern, da sie natürlichere Bewegungen ermöglichen. Darüber hinaus zeigt die tragbare Robotik spannende Fortschritte bei ihrem Design. Zum Beispiel verwenden softe Exoskelette weiche Materialien, um sowohl Sensoren als auch Aktuatoren zu erschaffen. Diese Arbeit erforscht ein adaptives Repräsentationsmodell für Bewegungen in der Robotik. Das konkrete Bewegungsmodell besteht aus vier Modularitäten: Musterauswahl, räumliche Koordination, zeitliche Koordination und sensorisch-motorische Anpassung. Diese Modularität in der Motorsteuerung könnte uns mehr Erkenntnisse über das Erlernen und Verallgemeinern von Handlungen nicht nur für humanoide Roboter, sondern auch für ihre biologischen Gegenstücke liefern. Erfolgreich testeten wir das Modell an einem humanoiden Roboter, indem dieser gelernt hat eine Vielzahl von Aufgaben auszuführen (Stoß-Ausgleichsbewegungen, Gehen, Zeichnen, Greifen, etc.). Im Folgenden schlagen wir mehrere weiche Aktuatoren vor, welche das Problem des Haltens schwerer Lasten und auch die Frage der Online- Programmierung der Roboterbewegung lösen. Diese weichen Aktuatoren verwenden textile Materialien mit thermoplastischem Polyurethan, die als aufblasbare Schläuche geformt sind. Die Schläuche wurden in Gehäusekanäle mit einer dehnungsbegrenzten Seite gefaltet, um Flexoren zu schaffen. Wir haben ein neues Design vorgeschlagen, um die angespannte Seite eines Flexors zu kontrollieren, indem wir vier textile Schnüre entlang seiner Längsachse hinzufügen. Dadurch kann das Verhalten des Flexors online programmiert werden, um ihn in mehrere Richtungen zu biegen und zu verdrehen. Im letzten Teil dieser Arbeit haben wir piezoresistive Elemente in einer Überlagerungsstruktur organisiert. Die sensorische Struktur wird auf einem sensorischen Greifer verwendet, um Druck- und Krümmungsreize zu erfassen und zu unterscheiden. Den sensorischen Greifer haben wir weiterentwickelt indem wir kapazitiv arbeitende Näherungssensoren mittels leitfähiger Textilteile hinzufügten. Schließlich entwickelten wir einen vielseitigen weichen Dehnungssensor, der Silikonschläuche mit einer eingebetteten resistiven Lösung verwendet, deren Wiederstand sich proportional zur Belastung der Schläuche verhält. Dies ermöglicht einem völlig weichen Handschuh die Erkennung von Handgesten. Die vorgeschlagenen Kombinationen aus weichen Aktuatoren, weichen Sensoren und biologisch inspirierter Bewegungsrepräsentation kann eine neue Perspektive eröffnen, um intelligente tragbare Roboter zu erschaffen.

<p>Soft pneumatic actuators allow compliant force application and movement for a variety of tasks. While most soft actuators have compliance in directions perpendicular to their direction of force application, they are most often analyzed only in their direction of actuation. In this work, we show a characterization of a soft 3D printed bellows actuator that considers shear and axial deformations, modeling both active and passive degrees of freedom. We build a model based on actuator geometry and a parallel linear and torsional spring system which we fit to experimental data in order to obtain the model constants. We demonstrate this model on two complex parallel networks, a delta mechanism and a floating actuator mechanism, and show how this single actuator model can be used to better predict movements in parallel structures of actuators. These results verify that the presented model and modeling approach can be used to speed up the design and simulation of more complex soft robot models by characterizing both active and passive forces of their one degree-of-freedom soft actuators.<br> </p>

Made available in DSpace on 2014-12-17T14:56:12Z (GMT). No. of bitstreams: 1 VictorLCMS_DISSERT.pdf: 2392559 bytes, checksum: f4276b98c00bcaaa7742d7ced860b2b7 (MD5) Previous issue date: 2013-04-19 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / A serious problem that affects an oil refinery s processing units is the deposition of solid particles or the fouling on the equipments. These residues are naturally present on the oil or are by-products of chemical reactions during its transport. A fouled heat exchanger loses its capacity to adequately heat the oil, needing to be shut down periodically for cleaning. Previous knowledge of the best period to shut down the exchanger may improve the energetic and production efficiency of the plant. In this work we develop a system to predict the fouling on a heat exchanger from the Potiguar Clara Camar?o Refinery, based on data collected in a partnership with Petrobras. Recurrent Neural Networks are used to predict the heat exchanger s flow in future time. This variable is the main indicator of fouling, because its value decreases gradually as the deposits on the tubes reduce their diameter. The prediction could be used to tell when the flow will have decreased under an acceptable value, indicating when the exchanger shutdown for cleaning will be needed / Um s?rio problema que afeta unidades de refino de petr?leo ? a deposi??o e incrusta??o de s?lidos nos equipamentos. Esses res?duos est?o naturalmente presentes no petr?leo ou s?o produtos de rea??es qu?micas durante o seu transporte. Um permutador de calor, quando sujo, perde sua capacidade de aquecer adequadamente o petr?leo, precisando, periodicamente, ser retirado de opera??o, para que possa ser realizada uma limpeza. Informa??es pr?vias do melhor per?odo para realizar as paradas podem melhorar a efici?ncia energ?tica e de produ??o da planta. Esse trabalho desenvolveu um sistema de predi??o da incrusta??o em um permutador da Refinaria Potiguar Clara Camar?o, com base em dados coletados em parceria com a Petrobras. Foram utilizadas redes neurais recorrentes que preveem a vaz?o no permutador em instantes futuros. Essa vari?vel ? o principal indicador da incrusta??o, pois seu valor diminui gradualmente ? medida que os dep?sitos nas paredes dos tubos reduzem seu di?metro. A predi??o pode ser usada para dizer quando a vaz?o ter? ca?do abaixo de um valor satisfat?rio, indicando quando ser? necess?rio retirar o equipamento de opera??o

Modellbasierte virtuelle Sensoren (VS) ermöglichen die Messung von qualitätsbestimmenden Prozessparametern (bzw. Hilfsregelgrößen) dort, wo eine direkte Messung zu teuer oder gar nicht möglich ist. Für die adaptiven VS, die ihr internes Prozessmodell nach Data-Driven-Methode bilden (z. B. durch die Benutzung künstlicher neuronaler Netze (KNN)), besteht das Problem der Abschätzung der Prädiktionsstabilität. Aktuelle Lösungsansätze lösen dieses Problem nur für wenige KNN-Typen und erfordern enormen Entwurfs- und Rechenaufwand. In dieser Arbeit wird eine alternative Methode vorgestellt, welche für eine breite Klasse von KNN gilt und keinen hohen Entwurfs- und Rechenaufwand erfordert. Die neue Methode wurde anhand realer Anwendungsbeispiele getestet und hat sehr gute Ergebnisse geliefert. Für die nicht adaptiven virtuellen Sensoren wurde eine aufwandsreduzierte Adaption nach Smith-Schema vorgeschlagen. Dieses Verfahren ermöglicht die Regelung totzeitbehafteter und zeitvarianter Prozesse mit VS in einem geschlossenen Regelkreis. Im Vergleich zu anderen Regelungsstrategien konnte damit vergleichbare Regelungsqualität bei einem deutlich geringeren Entwurfsaufwand erzielt werden. / Model-based virtual sensors allow the measurement of parameters critical for process quality where a direct measurement is too expensive or not at all possible. For the adaptive virtual sensors built after data-driven method (e.g., by use of an ANN model) there is a problem of the prediction stability. Current solutions attempt to solve this problem only for a few ANN types and require a very high development effort. In this dissertation a new method for the solution of this problem is suggested, which is valid for a wide class of the ANNs and requires no high development effort. The new method was tested on real application examples and has delivered very good results. For the non-adaptive virtual sensors a simple adaptation mechanism was suggested. This technique allows the control of dead-time and time-variant processes in closed loop. Besides, in comparison to other control strategies the comparable results were achieved with smaller development effort.

Modellbasierte virtuelle Sensoren (VS) ermöglichen die Messung von qualitätsbestimmenden Prozessparametern (bzw. Hilfsregelgrößen) dort, wo eine direkte Messung zu teuer oder gar nicht möglich ist. Für die adaptiven VS, die ihr internes Prozessmodell nach Data-Driven-Methode bilden (z. B. durch die Benutzung künstlicher neuronaler Netze (KNN)), besteht das Problem der Abschätzung der Prädiktionsstabilität. Aktuelle Lösungsansätze lösen dieses Problem nur für wenige KNN-Typen und erfordern enormen Entwurfs- und Rechenaufwand. In dieser Arbeit wird eine alternative Methode vorgestellt, welche für eine breite Klasse von KNN gilt und keinen hohen Entwurfs- und Rechenaufwand erfordert. Die neue Methode wurde anhand realer Anwendungsbeispiele getestet und hat sehr gute Ergebnisse geliefert. Für die nicht adaptiven virtuellen Sensoren wurde eine aufwandsreduzierte Adaption nach Smith-Schema vorgeschlagen. Dieses Verfahren ermöglicht die Regelung totzeitbehafteter und zeitvarianter Prozesse mit VS in einem geschlossenen Regelkreis. Im Vergleich zu anderen Regelungsstrategien konnte damit vergleichbare Regelungsqualität bei einem deutlich geringeren Entwurfsaufwand erzielt werden. / Model-based virtual sensors allow the measurement of parameters critical for process quality where a direct measurement is too expensive or not at all possible. For the adaptive virtual sensors built after data-driven method (e.g., by use of an ANN model) there is a problem of the prediction stability. Current solutions attempt to solve this problem only for a few ANN types and require a very high development effort. In this dissertation a new method for the solution of this problem is suggested, which is valid for a wide class of the ANNs and requires no high development effort. The new method was tested on real application examples and has delivered very good results. For the non-adaptive virtual sensors a simple adaptation mechanism was suggested. This technique allows the control of dead-time and time-variant processes in closed loop. Besides, in comparison to other control strategies the comparable results were achieved with smaller development effort.