<div>Previous studies have shown that implementing trajectory optimization can reduce state estimations errors. These navigation based path planning problems are often diffcult to solve being computationally burdensome and exhibiting other numerical issues, so former studies have often used lower-delity methods or lacked explanatory power.</div><div><br></div><div><div>This work utilizes indirect optimization methods, particularly optimal control theory, to obtain high-quality solutions minimizing state estimation errors approximated by a continuous-time extended Kalman lter. Indirect methods are well-suited to this because necessary conditions of optimality are found prior to discretization and numerical computation. They are also highly parallelizable enabling application to increasingly larger problems.</div></div><div><br></div><div><div>A simple one dimensional problem shows some potential obstacles to solving problems of this type including regions of the trajectory where the control is unimportant. Indirect trajectory optimization is applied to a more complex scenario to minimize location estimation errors of a single cart traveling in a 2-D plane to a goal location and measuring range from a xed beacon. This resulted in a 96% reduction of the location error variance when compared to the minimum time solution. The single cart problem also highlights the importance of the matrix that encodes the linearization of the vehicle's measurement with respect to state. It is shown in this case that the vehicle roughly attempts to maximize the magnitude of its elements. Additionally, the cart problem further illustrates problematic regions of a design space where the objective is not signicantly affected by the trajectory.</div></div><div><br></div><div><div>An aircraft descent problem demonstrates the applicability of these methods to aerospace problems. In this case, estimation error variance is reduced 28.6% relative to the maximum terminal energy trajectory. Results are shown from two formulations of this problem, one with control constraints and one with control energy cost, to show the benets and disadvantages of the two methods. Furthermore, the ability to perform trade studies on vehicle and trajectory parameters is shown with this problem by solving for dierent terminal velocities and different initial locations.</div></div>

O controle de plantas daninhas é uma importante preocupação para a agricultura tendo em vista as perdas de produtividade que estas causam ao competir com a cultura por água, luz e nutrientes. O uso de herbicida é a forma de manejo mais empregada em todo o mundo para o controle destas plantas. Entretanto, o uso frequente de um dado herbicida, além de causar diversos impactos ambientais, pode levar à diminuição da eficiência do próprio herbicida ao promover a seleção de plantas que são resistentes a este herbicida. Com o crescente número de novos casos de biótipos resistentes aos herbicidas, conter a evolução da resistência tornou-se uma necessidade para a agricultura convencional. Assim, grande esforço tem sido despendido para compreender este fenômeno e tentar contornar este problema. Neste sentido, os modelos computacionais se apresentam como importantes ferramentas para investigar os efeitos dos diversos fatores, em particular das estratégias de aplicação dos herbicidas, que influenciam na dinâmica da evolução da resistência. Com esta motivação, este trabalho tem como objetivo propor e estudar algumas estratégias de aplicação de herbicidas, ou ditos simplesmente controladores, que sejam implementáveis e que diminuam os impactos ambientais considerando a evolução da resistência. Para isto, assumimos que existe um herbicida, denominado neste trabalho por herbicida recomendado, que é o preferível dentre os disponíveis por produzir uma boa relação entre os benefícios produtivos e os malefícios aos ecossistemas. Para projetar os controladores, assumimos que é possível obter informações sobre a identificação visual da resistência em campo, feitas por um agente quando o número de indivíduos resistentes ultrapassa um certo limiar, assim como informações sobre a quantidade de plantas daninhas na área, feita possivelmente empregando técnicas de sensoriamento remoto. Então, para definir os controladores, empregamos diretamente a identificação visual da resistência e estimativas para o banco de sementes e para a fração dos genótipos do banco, geradas por um filtro de Kalman a partir de informações sobre a quantidade de plantas na área. Os controladores foram avaliados em relação à preservação da eficiência do herbicida recomendado, produtividade, impacto ambiental e propagação da resistência. Concluímos destes estudos que o controlador sugerido pode apresentar melhores resultados que os obtidos por controladores ditos convencionais, que se baseiam apenas na informação de identificação da resistência em campo. / Weed control is a major concern in agriculture as it causes significant loss of productivity by competition for water, sunlight and nutrients. The use of herbicides is the most common practice in the world to control them. However, the frequent use of a particular herbicide, besides causing many environmental impacts, may lead to loss of efficiency by promoting herbicide resistance via selection of resistant individuals. Considering the increasing number of herbicide resistant biotic, restraining resistance evolution is becoming a necessity for the conventional agriculture. This motivates a great deal of research effort to understand the involved phenomena and eventually to circumvent the problem. To this end, computational models are of great aid to understand the impact of many different aspects involved in this problem, in particular, to understand how different herbicide strategies usage lead to different resistance evolution dynamics. In this thesis we propose and study some strategies for herbicide application, which we refer to as controllers. We seek for controllers that can be implemented in real word crops growing, while decreasing environmental impacts and restrain resistance evolution. We assume that there exists one herbicide of choice for a given crop, meaning that it is preferred in terms of environmental impact and efficiency. To define the controllers, we assume that it is possible to obtain visual information on resistance, meaning that we observe when the proportion of resistant individuals is above a threshold. Also, we assume noisy observation of the number of adult weed individuals, possibly made by remote sensing. So, the controller directly employs the visual identification information and an estimate for the number of resistant seeds in the seed bank, generated by the Kalman filter using information on the number of adult weed. This strategy was evaluated in terms of herbicide efficiency preservation, crop production, environmental impact and resistance proliferation. We conclude that the proposed control strategies performed better than other strategies, called conventional strategies that are based only on the visual identification information.

O presente trabalho trata do desenvolvimento de um sistema de sensoriamento de orientação utilizando sensores inerciais de baixo custo, de tecnologia MicroElectroMechanical Systems, MEMS, que apresentam altas taxas de ruído. Assim, é realizada a filtragem e fusão dos dados dos sensores para obtenção de uma estimativa confiável, com a aplicação do filtro de Kalman estendido. O sistema é utilizado para a navegação e controle em um veículo aquático de superfície autônomo. No desenvolvimento do trabalho são investigados os princípios da navegação inercial, da representação da orientação e os sistemas de coordenadas envolvidos, apresentando o método por ângulos de Euler, quatérnios e DCM e o procedimento de atualização conforme a variação da orientação. O sistema desenvolvido foi testado em bancada e em um barco com formato de trimarã construído no Laboratório de Controle e Eletrônica de Potência, na Escola de Engenharia de São Carlos, mostrando os resultados dos testes realizados navegando em uma represa, obtendo resultados satisfatórios para essa aplicação. É mostrado também o comportamento dinâmico dos veículos aquáticos de superfície através do estudo da dinâmica de corpos rígidos. / This work describes the development of an orientation sensing system composed of low cost inertial sensors with MicroElectroMechanical Systems (MEMS) technology, which presents high noise levels. Thus, filtering and sensor\'s measurements fusion is done in order to achieve a reliable estimation, trough an extended Kalman filter. The system is used for navigation and control of an autonomous aquatic surface vehicle. In this work, the principles of inertial navigation, orientation representation as well as the coordinate frames involved are investigated, presenting the methods trough Euler angles, quaternions and DCM, and the update proceeding according to the orientation changes. The developed system was tested in the lab and on a trimaran shaped vessel navigating on a dam, wich was developed in the Control and Power Electronics Laboratory at the São Carlos School of Engineering, achieving satisfactory results for this application. It is also shown the dynamic behavior of the surface aquatic vehicles, using rigid-body dynamics.

Field programmable grid arrays (FPGA) are increasingly being adopted as the primary on-board computing system for autonomous deep space vehicles. There is a need to support several complex applications for navigation and image processing in a rapidly responsive on-board FPGA-based computer. This requires exploring and combining several design concepts such as systolic arrays, hardware-software partitioning, and partial dynamic reconfiguration. A microprocessor/co-processor design that can accelerate two single precision oating-point algorithms, extended Kalman lter and a discrete wavelet transform, is presented. This research makes three key contributions. (i) A polymorphic systolic array framework comprising of recofigurable partial region-based sockets to accelerate algorithms amenable to being mapped onto linear systolic arrays. When implemented on a low end Xilinx Virtex4 SX35 FPGA the design provides a speedup of at least 4.18x and 6.61x over a state of the art microprocessor used in spacecraft systems for the extended Kalman lter and discrete wavelet transform algorithms, respectively. (ii) Switchboxes to enable communication between static and partial reconfigurable regions and a simple protocol to enable schedule changes when a socket's contents are dynamically reconfigured to alter the concurrency of the participating systolic arrays. (iii) A hybrid partial dynamic reconfiguration method that combines Xilinx early access partial reconfiguration, on-chip bitstream decompression, and bitstream relocation to enable fast scaling of systolic arrays on the PolySAF. This technique provided a 2.7x improvement in reconfiguration time compared to an o-chip partial reconfiguration technique that used a Flash card on the FPGA board, and a 44% improvement in BRAM usage compared to not using compression.

<p>This thesis approaches the problem of estimating the arm angles of an industrial robot with flexibilities in joints and links. Due to cost-cutting efforts in the industrial robots industry, weaker components and more cost-effective structures have been introduced which in turn has led to problems with flexibilities, nonlinearities and friction. In order to handle these challenging dynamic problems and achieve high accuracy this study introduces state observers to estimate the tool position.The observers use measurements of the motor angles and an accelerometer and the different evaluated observers are based on an Extended Kalman Filter and a deterministic variant. They have been evaluated in experiments on an industrial robot with two degrees of freedom. The experimental verification shows that the state estimates can be highly accurate for medium frequency motions, ranging from 3-30Hz. For this interval the estimate were also robust to model inaccuracies.The estimation of low-frequency motions was relatively poor, due to problemswith drift for the accelerometer, and it also showed a significant dependence on the accuracy of the model. For industrial robots it is mainly the medium frequency motions which are hard to estimate with existing techniques and these observers therefore carries great potential for increased precision.</p>

<p><p>A permanent magnet synchronous motor is traditionally controlled from measured</p><p>values of the angular velocity and position of the rotor. However, there is a wish</p><p>from SAAB Avitronics to investigate the possibility of estimating this angular</p><p>velocity and position from the current measurements. The rotating rotor will</p><p>affect the currents in the motor’s stator depending on the rotor’s angular velocity,</p><p>and the observer estimates the angular velocity and angular position from this</p><p>effect.</p><p>There are several methods proposed in the article database IEEE Xplore to</p><p>observe this angular velocity and angular position. The methods of observation</p><p>chosen for study in this thesis are the extended Kalman filter and a phase locked</p><p>loop algorithm based on the back electro motive force augmented by an injection</p><p>method at low velocities.</p><p>The extended Kalman filter was also programmed to be run on a digital signal</p><p>processor in SAAB Avitronics’ developing hardware. The extended Kalman filter</p><p>performs well in simulations and shows promise in hardware implementation. The</p><p>algorithm for hardware implementation suffers from poor resolution in calculations</p><p>involving the covariance matrices of the Kalman filter due to the use of 16-bit</p><p>integers, yielding an observer that only functions in certain conditions.</p><p>As simulations with 32-bit integer algorithm performs well it is likely that a 32-</p><p>bit implementation of the extended Kalman filter would perform well on a motor,</p><p>making sensorless control possible in a wide range of operations.</p></p>

<p>Sensor fusion deals with the merging of several signals into one, extracting a better and more reliable result. Traditionally the Kalmanfilter is used for this purpose and the aircraft navigation has benefited tremendously from its use. This thesis considers the merge of two navigation systems, the GPS positioning system and the Saab developed Synthetic Attitude and Heading Reference System (SAHRS). The purpose is to find a model for such a fusion and to investigate whether the fusion will improve the overall navigation performance. The non-linear nature of the navigation equations will lead to the use of the extended Kalman filter and the model is evaluated against both simulated and real data. The results show that this strategy indeed works but problems will arise when the GPS signal falls away.</p>

<p>In several projects within ABB there is a need of state and parameter estimation for nonlinear dynamic systems. One example is a project investigating optimisation of gas turbine operation. In a gas turbine there are several parameters and states which are not measured, but are crucial for the performance. Such parameters are polytropic efficiencies in compressor and turbine stages, cooling mass flows, friction coefficients and temperatures. Different methods are being tested to solve this problem of system identification or parameter estimation. This thesis describes the implementation of such a method and compares it with previously implemented identification methods. The comparison is carried out in the context of parameter estimation in gas turbine models, a dynamic load model used in power systems as well as models of other dynamic systems. Both simulated and real plant measurements are used in the study.</p>

<p>To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. </p><p>In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. </p><p>In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.</p>

<p>Automotive positioning systems relying exclusively on the input from a GPS receiver, which is a line of sight sensor, tend to be sensitive to situations with limited sky visibility. Such situations include: urban environments with tall buildings; inside parking structures; underneath trees; in tunnels and under bridges. In these situations, the system has to rely on integration of relative motion sensors to estimate vehicle position. However, these sensor measurements are generally affected by errors such as offsets and scale factors, that will cause the resulting position accuracy to deteriorate rapidly once GPS input is lost. </p><p>The approach in this thesis is to use a GPS receiver in combination with low cost sensor equipment to produce a robust positioning module. The module should be capable of handling situations where GPS input is corrupted or unavailable. The working principle is to calibrate the relative motion sensors when GPS is available to improve the accuracy during GPS intermission. To fuse the GPS information with the sensor outputs, different models have been proposed and evaluated on real data sets. These models tend to be nonlinear, and have therefore been processed in an Extended Kalman Filter structure. </p><p>Experiments show that the proposed solutions can compensate for most of the errors associated with the relative motion sensors, and that the resulting positioning accuracy is improved accordingly.</p>