Neste trabalho, o equacionamento dinâmico de um manipulador espacial de base livre flutuante é descrito a partir do conceito do manipulador dinamicamente equivalente para que as técnicas de controle desenvolvidas sejam experimentalmente validadas em um manipulador convencional de base fixa. Dois tipos de controle de movimento são considerados. O primeiro foi desenvolvido no espaço das juntas e realiza o comando direto de posicionamento das juntas do manipulador; o segundo foi desenvolvido no espaço inercial e o controle é direcionado para o posicionamento do efetuador no espaço Cartesiano. Nos dois casos, o problema de acompanhamento de trajetória de um manipulador espacial com base livre flutuante sujeito a incertezas na planta e perturbações externas é proposto e solucionado sob o ponto de vista do critério de desempenho H \'INFINITO\'. Considerando métodos de controle para sistemas subatuados, três técnicas adaptativas foram desenvolvidas a partir de um controlador H \'INFINITO\' não-linear baseado na teoria dos jogos. A primeira técnica foi proposta considerando a estrutura do modelo bem definida, porém calculada com base em parâmetros incertos. Uma lei adaptativa foi aplicada para estimar esses parâmetros utilizando parametrização linear. Redes neurais artificiais são aplicadas nas outras duas abordagens adaptativas. A primeira utiliza uma rede neural para aprender o comportamento dinâmico do sistema robótico, considerado totalmente desconhecido. Nenhum dado cinemático ou dinâmico da base é utilizado neste caso. A segunda abordagem considera a estrutura do modelo nominal do manipulador bem definida e a rede neural é aplicada para estimar o comportamento das incertezas paramétricas e da dinâmica não-modelada da base. O critério H \'INFINITO\' é aplicado nas três técnicas para atenuar o efeito dos erros de estimativa. Resultados experimentais foram obtidos com um robô manipulador de base fixa subatuado (UArmII) e apresentaram melhor desempenho no acompanhamento da trajetória e no consumo de energia para as abordagens baseadas em redes neurais. / In the present work, the dynamics of a free-floating space manipulator is described through the dynamically equivalent manipulator approach in order to obtain experimental results in a planar fixed base manipulator. Control in joint and Cartesian spaces are considered. The first acts directly on joints positioning; the second control scheme acts on positioning the end-effector in some inertially fixed position. In both cases, the problem of tracking control with a guaranteed H-infinity performance for free-floating manipulator systems with plant uncertainties and external disturbances is proposed and solved. Considering control methods for underactuated systems, three adaptive techniques were developed from a nonlinear H-infinity controller based on game theory. The first approach was proposed considering a well defined structure for the plant, however it was computed based on uncertain parameters. An adaptive law was applied to estimate these parameters using linear parametrization. Artificial neural networks were applied in the two other approaches. The first one uses a neural network to learn the dynamic behavior from the robotic system, which is considered totally unknown. No kinematics or dynamics data from the spacecraft are necessary in this case. The second approach considers the nominal model structure well defined and the neural network is applied to estimate the behavior of the parametric uncertainties and of the spacecraft non-modeled dynamics. The H-infinity criterion was applied to attenuate the effect of estimation errors in the three techniques. Experimental results were obtained with an underactuated fixed-base planar manipulator (UArmII) and presented better performance in tracking and energy consumption for the neural based approaches.

L'objectif principal de ce travail est de proposer des approches de commande performantes et robustes aux incertitudes pour les robots parallèles de type Delta, qui sont conçus pour effectuer des tâches industriels importantes et exigeantes comme l'emballage en agroalimentaire, la découpe laser, etc. Les difficultés les plus importantes pour garantir une bonne performance de suivi de trajectoires de ces manipulateurs pour les hautes accélérations avec la meilleure précision possible, tout en conservant de telle performance indépendamment des conditions d'opération (par exemple avec différentes conditions de charge, différentes trajectoires, etc.) sont leur actionnement couplé, l'augmentation de leurs dynamiques non-linéaires et le problème de vibrations mécaniques avec l'augmentation des accélérations envisagées, la présence d'incertitudes sur le modèle/environnement et la redondance d'actionnement si elle existe. Dans cette thèse, différentes approches de commande et observateurs d'état ont été proposés et implémentés expérimentalement sur deux robots de type Delta, à savoir le Par2 (non-redondant) et le R4 (à redondance d'actionnement). Pour le premier, une commande non linéaire/adaptative à mode Dual a été proposée en espace articulaire, synthétisé avec trois différents observateurs d'état pour la estimation des vitesses articulaires: un observateur lead-lag, un observateur Alpha-bêta-gamma et un observateur à grand gain. Pour le robot R4, un commande à feedforward en espace-dual avec a été proposée pour la compensation de sa dynamique (avec laquelle une aaccélération maximale de 100G a été atteinte), puis un contrôleur adaptatif dans l'espace-dual a été proposé afin de garantir une estimation et mise à jours automatique des paramètres du système en temps réel, garantissant ainsi sa bonne performance indépendamment du scénario expérimental. L'analyse de stabilité du robot Par2 bouclé avec la commande adaptative à Mode Dual et du robot R4 commandé avec le contrôleur adaptatif dans l'espace-dual sont fournies, des simulations ont été effectuées et les résultats expérimentaux confirment la bonne performance des approches de commande proposées. / The main objective of this work is to propose control strategies performant and robust towards uncertainties for Delta-like parallel robots, which are designed to perform important and demanding industrial tasks, such as packaging, laser cutting, etc. The most important difficulties to guarantee the good tracking performance of these manipulators for very high accelerations with the best possible precision, while maintaining such performance independently of the operational case (e.g. with different load conditions, different trajectories, etc.) are their coupled actuation, the increase of their high nonlinear dynamics and the problem of mechanical vibrations with the increase of the involved accelerations, the presence of uncertainties in the model/environment and the redundant actuation when applicable. In this thesis, different control schemes and state observers were proposed and experimentally implemented on two Delta-like robots, namely the Par2 (non-redundant) and the R4 (redundantly actuated) parallel manipulators. For the former, a nonlinear/adaptive Dual Mode controller was proposed in the joint space, complied with three different state observers for the estimation of joint velocities: a Lead-lag based observer, an Alpha-beta-gamma observer and the High-gain observer. For the latter, firstly a dual-space feedforward controller was proposed for the compensation of its dynamics (with which a maximum of 100G of acceleration was reached), then a dual-space adaptive controller was proposed in order to automatically estimate the parameters of the system in real-time, thus guaranteeing its good performance independently of the experimental scenario. The stability analysis of Par2 robot under the control of the Dual Mode controller and the R4 robot under the control of the dual-space adaptive controller are provided, simulations were performed and the experimental results confirm the good performance of the proposed control schemes.

Macroscopic fundamental diagram is the concept of the highest importance in traffic flow theory used for development of network-wide control strategies. Previous studies showed that so called Arterial Fundamental Diagrams (AFDs) properly depict relationships between major macroscopic traffic variables on urban arterials. Most of these studies used detector’s occupancy as a surrogate measure to represent traffic density. Nevertheless, detector’s occupancy is not very often present in the field data. More frequently, field data from arterial streets provide performance metrics measured at the stop lines of traffic signals, which represent a hybrid of flow and occupancy. When such performance measures are used in lieu of density, the outcomes of the relationships between macroscopic fundamental variables can be confusing. This study investigates appropriateness of using degree of saturation, as a representative surrogate measure of traffic density, obtained from an adaptive traffic control system that utilizes stop-line detectors, for development of AFDs. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

A methodology to estimate the state of a moving marine vehicle, defined by its position, velocity and heading, from an unmanned surface vehicle (USV), also in motion, using a stereo vision-based system, is presented in this work, in support of following a target vehicle using an USV. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

For a wide range of people with limited upper- and lower-body mobility, interaction with robots remains a challenging problem. Due to various health conditions, they are often unable to use standard joystick interface, most of wheelchairs are equipped with. To accommodate this audience, a number of alternative human-machine interfaces have been designed, such as single switch, sip-and-puff, brain-computer interfaces. They are known as low throughput interfaces referring to the amount of information that an operator can pass into the machine. Using them to control a wheelchair poses a number of challenges. This thesis makes several contributions towards the design of robotic wheelchairs controlled via low throughput human-machine interfaces: (1) To improve wheelchair motion control, an adaptive controller with online parameter estimation is developed for a differentially driven wheelchair. (2) Steering control scheme is designed that provides a unified framework integrating different types of low throughput human-machine interfaces with an obstacle avoidance mechanism. (3) A novel approach to the design of control systems with low throughput human-machine interfaces has been proposed. Based on the approach, position control scheme for a holonomic robot that aims to probabilistically minimize time to destination is developed and tested in simulation. The scheme is adopted for a real differentially driven wheelchair. In contrast to other methods, the proposed scheme allows to use prior information about the user habits, but does not restrict navigation to a set of pre-defined points, and parallelizes the inference and motion reducing the navigation time. (4) To enable the real time operation of the position control, a high-performance algorithm for single-source any-angle path planning on a grid has been developed. By abandoning the graph model and introducing discrete geometric primitives to represent the propagating wave front, we were able to design a planning algorithm that uses only integer addition and bit shifting. Experiments revealed a significant performance advantage. Several modifications, including optimal and multithreaded implementations, are also presented.

Cette thèse porte sur le contrôle acoustique actif (ANC) dans une cavité. L’objectif est d’atténuer l’effet d’une onde sonore perturbatrice en des points ou dans un volume. Ceci est réalisé à l’aide d’un contre-bruit généré, par exemple, par un haut-parleur. Cette étude requiert l’utilisation de modèles dynamiques rendant compte de l’évolution des pressions aux points d’intérêt en fonction des bruits exogènes. Ce modèle peut être obtenu par une identification fréquentielle des réponses point-à-point ou en utilisant le modèle physique sous jacent (équation des ondes). Dans ce dernier cas, la recherche d'un modèle de dimension finie est souvent un préalable à l’étude conceptuelle d'un système d’ANC. Les contributions de cette thèse portent donc sur l’élaboration de différents modèles simplifiés paramétrés par la position pour les systèmes acoustiques et sur la conception de lois de commande pour l’ANC. Le premier volet de la thèse est dédié à l’élaboration de différents modèles simplifiés de système de propagation acoustique au sein d’une cavité. Pour cela, les simplifications envisagées peuvent être de nature spatiale autant que fréquentielle. Nous montrons notamment qu'il est possible, sous certaines conditions, d’approximer le système 3D par un système 1D. Ceci a été mis en évidence expérimentalement sur le banc d’essai LS2NBox. Le second volet porte sur la conception de lois de commande. En premier lieu, les stratégies de commandes couramment utilisées pour l’ANC sont comparées. L'effet dela commande multi-objectif H en différents points voisins des points d'atténuation est analysé. La possibilité d’une annulation parfaitedu bruit en un point est aussi discutée. / This thesis deals with active noise control (ANC) in a cavity. The aim is to mitigate the effect of a disturbing sound wave at some points or in a volume. This is achieved using ananti-noise generated, for example, by a loudspeaker. This study requires the use of dynamic models that report changes in pressure at points of interest in response to exogenous noises. Such models can be obtained by frequency identification of point-to-point responses or by using the underlying physical model (wave equation). In the latter case, the search for a low-complexity model (finite dimensional model) is often a prerequisite for the conceptual study of an active control system. The contributions of this thesis concern the development of different simplified models parameterized by the spatial position for acoustic systems, and the design of control laws for noise attenuation. The first part of the thesis is dedicated to the development of various simplified models of acoustic propagation system within a cavity. For that, the simplifications envisaged can be of spatial nature as much as frequential. We show in particular that it is possible, under certain conditions, to approximate the 3D system by a 1D system. This has been demonstrated experimentally on the prototype system, LS2NBox. The second part of the thesis deals with the design of control laws. First, the control strategies commonly used for ANC are compared. The effect of multi-objective H control at different spatial positions close to the attenuation points is analyzed. The possibility of perfect noise cancellation at one point is also discussed.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.

Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-07-18T19:13:43Z No. of bitstreams: 1 HiltonSantos.pdf: 3137200 bytes, checksum: a7b77b12eeb29959ab49e7ef675229d9 (MD5) / Made available in DSpace on 2017-07-18T19:13:43Z (GMT). No. of bitstreams: 1 HiltonSantos.pdf: 3137200 bytes, checksum: a7b77b12eeb29959ab49e7ef675229d9 (MD5) Previous issue date: 2017-06-27 / The emergence of new industrial plants with great complexity and the need to improve the operation of existing plants has fostered the development of high performance control systems, these systems must not only meet the design specifications, such as merit figures, but also operate at minimal cost and impacts at environment. Motivated by this demand, it is presented in this dissertation the development of methods for on-line tuning of control system parameters, ie, a methodology is presented for the on-line tuning of adaptive and optimal PID controllers via Artificial Neural Networks(ANNs). The approach developed in this dissertation is based on three PID controllers parameters. [Artificial neural networks with radial base functions and Model Predictive Control (MPC). From the union of these approaches a general formulation of an Adaptive-optimal PID controller via artificial neural networks with on-line tuning was presented. The on-line tuning methodology for the ANN parameters is presented in the context of MPC, predicting plant output. For the PID controller, we proposed a modification of the standard structure in order to adapt the error function. The adjustment of the PID controller parameters and the prediction of the optimally plant output, are performed by the ANN-RBF weights adjustments. In addition, an indoor implementation of the control system were proposed for the positioning of a photovoltaic panel. The performance evaluations of the proposed system were obtained from computational experiments results that were based on mathematical models and hardware experiments, that were obtained from a reduced model of a photovoltaic panel. Finally, a comparison between the proposed methodology with the classical PID controller were performed and the proposed methodology presented to be more flexible to the insertion of new performance metrics and the results achieved from the ANN, were better than the ones obtained by the classical PID tuning, such as: Ziegler-Nichols or trial and error. / O surgimento de novas plantas industriais com grande complexidade e a necessidade de melhorar a operação das plantas já existentes tem fomentado o desenvolvimento de sistemas de controle de alto desempenho, estes sistemas devem atender não só as especificações de projeto, tal como: figuras de mérito, mas também devem operar com um custo mínimo e sem causar impactos desastrosos para o meio ambiente. Motivados por esta demanda, apresenta-se nesta dissertação o desenvolvimento de métodos para sintonia online dos parâmetros dos sistemas de controle, ie, apresenta-se uma metodologia para a sintonia online de controladores PID adaptativo e ótimo via Redes Neurais Artificiais (RNAs). A abordagem desenvolvida nesta dissertação tem base as ações dos controladores PID de três termos, redes neurais artificiais com funções de base radial e Controle preditivo baseado em modelo (MPC - Model Predictive Control), a partir da união destas abordagens elabora-se a formulação geral do controlador PID Adaptativo-Ótimo via redes neurais artificiais, com sintonia online. A metodologia de ajuste online dos parâmetros da RNA está no contexto do MPC para predição de saída da planta. Para o caso do controlador PID, tem-se a modificação da estrutura padrão com o objetivo de adaptação em função do erro. O ajuste dos termos do controlador PID e da predição da saída na planta, de forma ótima, é realizada pelo ajustes dos pesos da RNA-RBF. Além disso, apresenta-se a implementação indoor do sistema de controle desenvolvido para o posicionamento de um painel fotovoltaico. As avaliações de desempenho do sistema proposto são obtidos de resultados de experimentos computacionais que são baseados em modelos matemáticos e experimentos em hardware que são obtidos de um modelo reduzido de um painel fotovoltaico. Por fim, comparando o PID clássico com o controlador desenvolvido constatou-se que este último apresenta mais flexibilidade para inserir novas métricas de desempenho e os resultados atingidos são melhores do que os parâmetros obtidos por meio da sintonia do PID clássica, tais como: métodos de Ziegler-Nichols ou tentativa e erro

Submitted by Sandro Camargo (sandro.camargo@unipampa.edu.br) on 2015-05-09T20:45:31Z No. of bitstreams: 1 117110026.pdf: 2752105 bytes, checksum: bd6b8c2341ad7ebc5b553fad3338b56f (MD5) / Made available in DSpace on 2015-05-09T20:45:31Z (GMT). No. of bitstreams: 1 117110026.pdf: 2752105 bytes, checksum: bd6b8c2341ad7ebc5b553fad3338b56f (MD5) Previous issue date: 2014-12-01 / O controle de conversores eletrônicos de potência tem recebido muita atenção devido às suas inúmeras aplicações. Destacam-se especialmente aplicações em problemas de qualidade de energia, onde é necessário injetar uma corrente na rede elétrica de acordo com uma referência. A conexão de conversores na rede elétrica, no entanto, apresenta diversos desafios, como a existência de incerteza paramétrica na planta e distúrbios advindos da rede. Além disso, inerentemente ao seu funcionamento, conversores eletrônicos de potência geram componentes harmônicas de comutação que precisam ser filtradas. A tendência atual das estratégias de controle é o relaxamento da exigência clássica de conhecimento completo da planta a ser controlada, buscando robustez com relação às incertezas paramétricas. Este trabalho apresenta uma estratégia de controle capaz de rejeitar distúrbios e apresentar bom desempenho frente a incertezas, utilizando técnicas de controle multimalhas e controle adaptativo. Por fim, são apresentados resultados de simulação, e resultados experimentais que mostram o bom funcionamento do sistema. / Voltage-source converter control is being very exploited due to its numerous applications. Special attention is given to energy quality applications, which demand the injection of currents in the grid according to a reference current. The connection of converters to the grid, however, presents several challenges such as parametric uncertainty associated to the plant and disturbances coming from the grid. Furthermore, inverters generate switching harmonics that need to be filtered. The tendency in control strategies is the relaxation of the classical requirement of complete knowledge of the plant, seeking robustness with respect to parametric uncertainties. This work presents a control strategy capable of disturbance rejection and good performance in relation to uncertainties, using Multi-Loop and Adaptive control techniques. Simulation results are presented, and experimental results show the good behavior of the proposed system.

Contrairement aux robots manipulateurs industriels qui sont de taille énorme et de prix élevé, beaucoup de robots manipulateurs à bas prix sont déjà entrés dans le marché, avec une petite taille, un poids léger, ce type de robots est plus accessible pour les particuliers. Cependant, limité par le coût de revient, des accessoires (matériaux, actuateurs, contrôleurs, etc) adoptés sont aussi limités, cela conduit souvent à la performance moins robuste au niveau de contrôle. Cette thèses se concentre sur la conception de contrôleur pour améliorer la performance des robots manipulateurs à bas prix. D'abord, pour des robots manipulateurs rigides, la modélisation dynamique en lien avec le système d'actualisation est établie, qui forme une équation différentielle avec paramètres constants et perturbation. Une méthode d'identification des paramètres en utilisant des observateurs et une commande adaptative sont proposées, et des résultats de simulation et d'expérimentation sont donnés. Ensuite, pour le cas d'articulation flexibles, pour simplifier, le modèle 1DOF est pris en compte. Premièrement, avec la mesure de la vitesse de lien, une méthode d'identification et une loi deux-étages adaptative sont proposées à condition que la position statique de lien puisse également être mesurée, des résultats de simulation sont donnés. Deuxièmement, en utilisant des mesures d'accélération de lien, une méthode d'identification et la même loi deux-étages adaptative sont proposées, cette idée est généralisée à l'identification et au contrôle de systèmes linéaires avec mesures de dérivées d'ordre élevé, des résultat de simulation sont présentés. Pour la mise en œuvre, des capteurs inertiels (gyroscopes et accéléromètres) sont utilisés et des résultats expérimentaux sont présentés. / Unlike industrial robot manipulators which are huge in size and of high price, many low-cost robot manipulators have already entered the market, with small size and light weight, this type of robots are more accessible to the public. However, limited by the cost, the components adopted (materials, actuators, controllers, etc.) are also limited, this often leads to less robust control performance. This thesis focuses on the controller design to improve the performance for such kind low-cost robot manipulators. To start with, for rigid case, dynamic modeling considering the actuator system is established, which forms a differential equation with constant parameters and disturbance, a method to identify the model parameters using observers and then an adaptive controller are proposed, simulation and experimental results are given. Then, in case of flexible joints, for simplicity, a single-link case model is considered. Firstly, link velocity measurement is assumed to provide link information, and an identification method and a two-stage adaptive control low are proposed provided that the static link position can also be measured, simulation result is given. Secondly, by using link acceleration measurement, an identification method and the same two-stage adaptive control low areproposed, this idea is generalized to identification and control of linear system using high-order derivative measurements, simulation result is presented. For implementation, inertial sensors (gyro and accelerometer) are used and experimental result is presented.