Dissertations / Theses on the topic 'Data controllers'

This dissertation evaluates evolutionary methods for evolving cooperative teams of robots. Cooperative robotics is a challenging research area in the field of artificial intelligence. Individual and autonomous robots may by cooperation enhance their performance compared to what they can achieve separately. The challenge of cooperative robotics is that performance relies on interactions between robots. The interactions are not always fully understood, which makes the designing process of hardware and software systems complex. Robotic soccer, such as the RoboCup competitions, offers an unpredictable dynamical environment for competing robot teams that encourages research of these complexities. Instead of trying to solve these problems by designing and implement the behavior, the robots can learn how to behave by evolutionary methods. For this reason, this dissertation evaluates evolution of neural controllers for a team of two robots in a competitive soccer environment. The idea is that evolutionary methods may be a solution to the complexities of creating cooperative robots. The methods used in the experiments are influenced by research of evolutionary algorithms with single autonomous robots and on robotic soccer. The results show that robot teams can evolve to a form of cooperative behavior with simple reactive behavior by relying on self-adaptation with little supervision and human interference.

n this dissertation a comparison of two evolutionary methods for evolving ANNs for robot control is made. The methods compared are SANE with enforced sub-population and delta-coding, and marker-based encoding. In an attempt to speed up evolution, marker-based encoding is extended with delta-coding. The task selected for comparison is the hunter-prey task. This task requires the robot controller to posess some form of memory as the prey can move out of sensor range. Incremental evolution is used to evolve the complex behaviour that is required to successfully handle this task. The comparison is based on computational power needed for evolution, and complexity, robustness, and generalisation of the resulting ANNs. The results show that marker-based encoding is the most efficient method tested and does not need delta-coding to increase the speed of evolution process. Additionally the results indicate that delta-coding does not increase the speed of evolution with marker-based encoding.

This work treats modelling of electromagnetic fields from controlled sources in geophysical applications. The focus is on modelling the marine CSEM (controlled source electromagnetic) method in planarly layered media. The recent introduction of SeaBed Logging (SBL) as an application of the marine CSEM method for direct hydrocarbon identification has resulted in increased survey activity, and expanded as well as renewed the interest for investigating electromagnetic field propagation in the subsurface of the earth.

The material within this document consists of a short introduction to the CSEM and SBL methods and four self-contained papers:

• Low-frequency electromagnetic fields in applied geophysics: Waves or diffusion? treats propagation of low-frequency fields in conductive media, and compares their behaviour to nondistorted field propagation in lossless media.

• The first test of the SeaBed Logging method describes the first laboratory test of this method. The scaled experiment was performed in order to validate if the detection of thin resistive layers within conductive surrounding media is possible.

• Asymptotic evaluations of the marine CSEM field integrals elaborates on how electromagnetic signals propagate in an idealized stratified earth model. To this end, the method of steepest descents is applied in order to separate the various wavemodes.

• Electromagnetic fields in planarly layered anisotropic media formulates a mathematical description of the field propagation in stratified media with arbitrary anisotropy. The field equations are solved by using the matrix propagator technique.

Even if electromagnetic field propagation in layered media is a rather mature research subject, the current development of the CSEM and SBL methods demands reinvestigations and new theoretical insights. Optimal survey planning and solid interpretation rely on a thorough understanding of the signal propagation in the subsurface. The main motivation in this thesis is to contribute to increased knowledge of how electromagnetic fields travel in the earth.

Artificial pancreas (AP) systems are implemented as a treatment for type 1 diabetes (T1D) patients to regulate blood glucose concentration (BGC). With continuous glucose monitoring (CGM), information related to BGC can be measured at a high frequency. It is widely known that besides meals, BGC is also influenced by many other factors such as exercise, sleep, and stress. In order to get information about these factors, different kinds of measurements such as heart rate, acceleration and derived variables such as energy expenditure (EE) should also be collected using equipment like armband and chest band devices to be used as inputs for AP systems. With adequate information about patients, BGC, and other related factors, the controller in AP systems is able to calculate insulin infusion rate for patients based on the model and control algorithm. The insulin pump will deliver the calculated amount of insulin to patient's body to close the loop of BGC regulating.

For AP systems, the performance of model-based control systems depends on the accuracy of the models and may be affected when large dynamic changes in the human body occur or when the equipment performance varies. And those factors may move the operating conditions away from those used in developing the models and designing the control system. Sensor errors such as signal bias and missing data can mislead or stop the calculation of insulin infusion rate. All of these possible performance failures can make AP systems unreliable and endanger the safety of patients.

This project aims to develop additional modules focused on fault detection and diagnosis of the controller and the sensors of the AP system. A controller performance assessment module (CPAM) is developed to generate several indexes to monitor different aspects of controller performance and retune the controller parameters according to different types of controller performance deterioration. A sensor error detection and reconciliation module (SED&RM) is developed to detect sensor error in CGM measurements. The SED&RM is based on two model estimation technologies, outlier-robust Kalman filter (ORKF) and locally weighted partial least squares (LW-PLS) to replace the erroneous sensor signal with the model estimated value. A novel method, the nominal angle analysis (NAA) is introduced to solve problems of false positive and candidate selection for signal reconciliation. SED&RM is extended to multi-sensor error detection and reconciliation module (MSED&RM), which also includes error detection and reconciliation for other sensor signals such as galvanic skin response (GSR) and values derived from original sensor signals such as EE. A multi-level supervision and controller modification (ML-SCM) module integrates CPAM and MSED&RM together and extends the controller modification into different time scales including sample level, period level, and day level.

CPAM is tested with a single input and single output (SISO) version of AP system in UVa/Padova simulator. The results indicate that a generalized predictive control (GPC) with the proposed CPAM has a safer range of glucose concentration variation and more reasonable insulin suggestions than a GPC without controller retuning guided by the proposed CPAM. The performance of SED&RM and MSED&RM is tested with data from clinical experiments. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable model estimation values. The ML-SCM is tested with both simulation and clinical experiments. The results indicate that the AP system with ML-SCM module has a safer range of glucose concentration distribution and more reasonable insulin infusion rate suggestions than an AP system without the ML-SCM module.

Look ahead cruise control deals with the concept of using recorded topographic road data combined with a GPS to control vehicle speed. The purpose of this is to save fuel without a change in travel time for a given road. This thesis explores the sensitivity of different disturbances for look ahead systems. Two different systems are investigated, one using a simple precalculated speed trajectory without feedback and the second based upon a model predictive control scheme with dynamic programming as optimizing algorithm.

Defect input data like bad positioning, disturbed angle data, faults in mass estimation and wrong wheel radius are discussed in this thesis. Also some investigations of errors in the environmental model for the systems are done. Simulations over real road profiles with two different types of quantization of the road slope data are done. Results from quantization of the angle data in the system are important since quantization will be unavoidable in an implementation of a topographic road map.

The results from the simulations shows that disturbance of the fictive road profiles used results in quite large deviations from the optimal case. For the recorded real road sections however the differences are close to zero. Finally conclusions of how large deviations from real world data a look ahead system can tolerate are drawn.

Controlled Source Tensor Magnetotelluric (CSTMT) ground measurements were executed on the Hallandsåsen horst where a major tunnel is under construction. The instrument system EnviroMT are used for this purpose. The major research aspect of this thesis has been to form an opinion of the effectiveness of the method by comparing the results from the CSTMT survey with a prior investigation performed with the DC resistivity method. Another important part of this thesis has been to compile the basic and fundamental CSTMT and RMT theory, in a way that people outside the EM community easily can be introduced to the subject.

When comparing the different inversion models from the CSTMT and DC resistivity surveys one can see differences in the depth at which the conductors are resolved. In the CSTMT inversion models (TE+TM) there are two conductors that possibly can reach the depth of the tunnel in construction. These conductors are not resolved at the deeper structures in the DC resistivity models. Whether the conductors in the CSTMT inversions (TE+TM) truly extend to the depth at which they are modeled, or if they in deeper parts are artificial effects of regularisation in the inversion cannot be said for sure. Accounting for the low frequencies utilised in the TE mode, one has very strong arguments that the deep conductors seen in the CSTMT model are true.

The TE-mode models have shown to be much less affected by the complex problems of near field effects in comparison with the TM-mode models. The evidence of the near field effects is very prominent in the TM-mode phase, but in the phase of the TE-mode one can not see any such tendencies. However, one can see a discontinuity in the same part of three profile lines which shows that the data is disturbed but not nearly as much as in the TM-mode. The apparent resistivity seems to be over all less affected by the near field effects. In the apparent resistivity of the TE-mode, one can not discern any near field effects at all.

In the TM-mode, the apparent resistivity shows higher apparent resistivity than the real apparent resistivity in the near field. To receive more information about the deeper structures, lower controlled source frequencies were allowed in the TE-mode than in the TM-mode inversion models. The RMS in the TE-mode inversions has not been deteriorated, which is an another indication that the TE mode is not very disturbed by the near field effects.

The RMT inversion models are shown to be heavily biased in the deeper parts to which the RMT data are insensitive and regularization determining the outcome of the inversion. One can also see that regularisation is influencing the whole inversion model. In the shallow subsurface the inversion models should be same for CSTMT and RMT, but one can see differences in resistivity between the models.

The real induction arrows show features that are not as clearly displayed in either the phase or apparent resistivity. It seems that the real induction arrows are better at detecting lateral differences in conductivity in a more resistive media, than the phase and apparent resistivity.