One method to increase efficiency, robustness and accuracy of automatic control, is to introduce mathematical models of the system in question to increase performance. With these models, it is possible to predict the behavior of the system, which enables control according to the predictions. The problem here is that if these models do not describe the dynamics of the system well enough, this method could fail to increase performance. To address this problem, one idea is to estimate the dynamics of the system during operation, using methods for system identification, signal processing and sensor fusion. In this thesis, the possibilities of estimating a ship's dynamics during operation have been investigated. The mathematical model describing the dynamics of the ship is a graybox model, which is based on the physical and mechanical relations. This model's properties are therefore described by physical quantities such as mass and moment of inertia, all of which are unknown. This means that, when estimating the model, these physical properties will be estimated. For a systematic approach, first a simulation environment with a 4-degrees-of-freedom ship model has been developed. This environment has been used for validation of system identification methods. A model of a podded propulsion system has also been derived and validated. The methods for estimating the properties of the ship have been analyzed using the data collected from the simulations. For system identification and estimation of ship properties, the influence of measurement noise and potential of detecting a change in dynamics has been analyzed. This has been done through Monte Carlo simulations of the estimation method with different noise realizations in the simulations, to analyze how the measurement noise affects the variance and bias for the estimates. The results show that variance and bias vary a lot between the parameters and that even a small change in dynamics is visible in some parameter estimates when only ten minutes of data have been used. A method based on cumulative summation (CUSUM) has been proposed and validated to analyze if such a method could yield fast and effective detection of system deviations. The results show that the method is rather effective a with robust detection of changes in the dynamics after about four minutes of data collection. Finally, the methods have been validated on data collected on a real ship to analyze the potential of the methods under actual circumstances. The results show that the particular data is not appropriate for this kind of application along with some additional problems that can yield impaired results. / Genom att inkludera matematiska modeller som beskriver ett systems dynamik i styrningsalgoritmer, kan man åstadkomma en automatisk styrning med förbättrad effektivitet, robusthet och noggrannhet. Med dessa modeller går det att förutsäga beteendet hos systemet och därmed öppnas också möjligheten att använda sig av detta i styrningen. Problemet är att om dessa modeller inte beskriver systemets dynamik tillräckligt bra kan prestandan istället sänkas genom dessa metoder. Den här sortens problem kan man lösa genom att aktivt skatta systemets dynamik under körning, med hjälp av metoder för systemidentifiering, signalbehandling och sensorfusion. I denna exjobbsrapport har möjligheterna att skatta ett skepps girdynamik undersökts. Den matematiska modell som beskriver skeppets dynamik är en grålådemodell som baserar sig på fysikaliska och mekaniska samband. Denna modells egenskaper beskrivs därför av fysikaliska storheter så som massa, tröghetsmoment och tyngdpunkt, vilka alla är okända. Detta innebär att vid modellskattning skattas dessa fysikaliska storheter, vilka kan vara av stort intresse. En simuleringsmiljö med en skeppsmodell med fyra frihetsgrader har skapats och använts för att validera metoder för systemidentifiering. En modell av ett roterbart framdrivningssystem har också härletts och inkluderats i simuleringsmodellen. Vid systemidentifiering och skattning av skeppets egenskaper har dels inverkan av mätbrus analyserats samt även möjligheter till att detektera skillnader i dynamik. Detta har gjorts med Monte Carlo-simuleringar av skattningsmetoden med olika brusrealiseringar för att analysera hur mätbrus påverkar variansen och metodfelet hos skattningarna. Resultaten visar att vissa parametrar skattas med större noggrannhet och hos dessa kan därmed en förändring i dynamik identifieras när endast tio minuter av data har använts. En metod baserad på kumulativ summering av residualer har formulerats och validerats, detta för att undersöka om en sådan metod kan ge snabb och effektiv detektion av systemförändringar. Resultat visar på robusthet i att detektera skillnader i dynamik efter ungefär fyra minuter av datainsamling. Slutligen har metoderna validerats på data insamlad på ett riktigt skepp för att undersöka potentialen under verkliga omständigheter. Resultaten visar att just denna data inte är lämplig för denna applikation samt några problem som kan leda till försämrade resultat.

Following the debate by empirical finance research on the presence of non-linear predictability in stock market returns, this study examines forecasting abilities of nonlinear STAR-type models. A non-linear model methodology is applied to daily returns of FTSE, S&P, DAX and Nikkei indices. The research is then extended to long-horizon forecastability of the four series including monthly returns and a buy-and-sell strategy for a three, six and twelve month holding period using non-linear error-correction framework. The recursive out-of-sample forecast is performed using the present value model equilibrium methodology, whereby stock returns are forecasted using macroeconomic variables, in particular the dividend yield and price-earnings ratio. The forecasting exercise revealed the presence of non-linear predictability for all data periods considered, and confirmed an improvement of predictability for long-horizon data. Finally, the present value model approach is applied to the housing market, whereby the house price returns are forecasted using a price-earnings ratio as a measure of fundamental levels of prices. Findings revealed that the UK housing market appears to be characterised with asymmetric non-linear dynamics, and a clear preference for the asymmetric ESTAR model in terms of forecasting accuracy.

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.

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Approved for public release, distribution is unlimited / occurring most frequently. For the 217 large-error cases due to midlatitude influences, the most frequent error mechanisms were E-DCI (midlatitude), excessive response to vertical wind shear, excessive midlatitude cyclogenesis (E-MCG), insufficient midlatitude cyclogenesis (I-MCG), excessive midlatitude cyclolysis (E-MCL) and excessive midlatitude anticyclogenesis (E-MAG), which accounted for 68% of all large errors occurring in both NOGAPS and GFDN. Characteristics and symptoms of the erroneous forecast tracks and model fields are documented and illustrative case studies are presented. Proper identification and removal of the track forecast displaying an error mechanism could form a selective consensus that will be more accurate than a non-selective consensus. / Captain, United States Air Force