When a helicopter is flying, the dynamics vary depending on, for example, speed and position. Hence, a time-invariant linear model cannot describe its properties under all flight conditions. It is therefore desirable to update the linear helicopter model continuously during the flight. In this thesis, two different recursive estimation methods are presented, LMS (Least Mean Square) and adaptation with a Kalman filter. The main purpose of the system estimation is to get a model which can be used for feedback control. In this report, the estimated model will be used to create a LQ controller with the task of keeping the output signal as close to the reference signal as possible.Simulations in this report show that adaptive feedback control can be used to control a helicopter's angular velocities and that the possibility to use an adaptive control algorithm in a real future helicopter is good.

Simultaneous Localization And Mapping (SLAM) is a process of mapping an unknown environment and at the same time keeping track of the position within this map. In this theses, SLAM is performed in a marine environent using radar images only. A SLAM solution is presented. It uses SIFT to compare pairs of radar images. From these comparisons, measurements of the boat movements are obtained. A type of Kalman filter (Exactly Sparse Delayed-state Filter, ESDF) uses these measurements to estimate the trajectory of the boat. Once the trajectory is estimated, the radar images are joined together in order to create a map. The presented solution is tested and the estimated trajectory is compared to GPS data. Results show that the method performs well for at least shorter periods of time.

Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor. A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter. The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful. The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity. All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.

Optical encoders are being widely suggested for precise mobile navigation. Combining such sensor information with Global Positioning System (GPS) is a practical solution for reducing the accumulated errors from encoders and moving the navigational base into global coordinates with high accuracy. This thesis presents integration methods of GPS and optical encoders for a mobile vehicle that is controlled by microcontroller. The system analyzed includes a commercial GPS receiver, dsPIC microcontroller and mobile vehicle with optical encoders. Extended kalman filtering (EKF), real time curve matching, GPS filtering methods are compared and contrasted which are used for integrating sensors data. Moreover, computer interface, encoder interface and motor control module of dsPIC microprocessor have been used and explained. Navigation quality on low speeds highly depends greatly upon the processing of GPS data. Integration of sensor data is simulated for both EKF and real time curve matching technique and different behaviors are observed. Both methods have significantly improved the accuracy of the navigation. However, EKF has more advantages on solving the localization problem where it is also dealing with the uncertainties of the systems.

In modern cars, advanced driver assistance systems are used to aid the driver and increase the automobile safety. An example of such a system is the night vision system designed to detect and warn for pedestrians in danger of being hit by the car. To determine if a warning should be given when a pedestrian is detected, the system requires a prediction of the future path of the car for up to four seconds ahead in time. In this master's thesis, a new path prediction algorithm based on satellite positioning and a digital map database has been developed. The algorithm uses an extended Kalman filter to get an accurate estimate of the current position and heading direction of the car. The estimate is then matched to a position in the map database and the possible future paths of the vehicle are predicted using the road network. The performance of the path prediction algorithm has been evaluated on recorded night vision sequences corresponding to 15 hours of driving. The results show that map-based path prediction algorithms are superior to dead-reckoning methods for longer time horizons. It has also been investigated whether vision-based lane detection and tracking can be used to improve the path prediction. A prediction method using lane markings has been implemented and evaluated on recorded sequences. Based on the results, the conclusion is that lane detection can be used to support a path prediction system when lane markings are clearly visible.

High precision state estimation is crucial when executing drift control and high speed control close to the stability limit, on electric RC scale cars. In this thesis the estimation is made possible through recursive Bayesian filtering; more precisely the Extended Kalman Filter. By modelling the dynamics of the car and using it together with position measurements and control input signals, it is possible to do state estimation and prediction with high accuracy even on non-measured states. Focus is on real-time, on-line, estimation of the so called slip angles of the front and rear tyres, because of their impact of the car’s behaviour. With the extended information given to the system controller, higher levels of controllability could be reached. This can be used not only for higher speeds and drift control, but also a possibility to study future anti-skid safety measures forground vehicles.

The popularity of wireless sensor networks is constantly increasing, both for use instatic machine to machine environments as well as dynamic environments wherethe sensor nodes are carried by humans. Higher demands are put on real-timetracking algorithms of the sensor nodes, both in terms of accuracy and speed. This thesis addresses the issue of tracking persons wearing small sensor nodeswithin a radio network. Focus lies on fusing sensor data in an efficient way withconsideration to the computationally constrained sensor nodes. Different sensorsare stochastically modelled, evaluated, and fused to form an estimate of the person’sposition. The central approach to solve the problem is to use a dead reckoning methodby detecting steps taken by the wearer combined with an Inertial MeasurementUnit to calculate the heading of the person wearing the sensor node. To decreasethe unavoidable drift which is associated with a dead reckoning algorithm, a mapis successfully fused with the dead reckoning algorithm. The information from themap can to a large extent remove drift. The developed system can successfully track a person wearing a sensor nodein an office environment across multiple floors. This is done with only minorknowledge about the initial conditions for the user. The system can recover fromdivergence situations which increases the long term reliability. / Intresset för trådlösa sensornätverk ökar konstant, såväl för statiska maskintill-maskintillämpningar som för dynamiska miljöer där sensornoderna är burnaav människor. Allt högre krav ställs på positioneringsalgoritmer för sensornätverken,där både hög precision och låg beräkningstid ofta är krav. Denna rapport behandlar problemet med att bestämma positionen av personburnasensornoder. Rapportens fokus är att effektivt kombinera sensordatamed hänsyn till sensornodernas begränsade beräkningskapacitet. Olika sensorermodelleras stokastiskt, utvärderas och kombineras för att forma en skattning avsensornodens position. Den huvudsakliga metoden för att lösa problemet är att dödräkna sensornodbärarenssteg kombinerat med kompass och tröghetssensorer för att skattastegets riktning. En karta över byggnaden används för att reducera den annarsoundvikliga drift som härrör från dödräkning. Informationen från kartan visarsig i stor utsträckning kunna reducera den här driften. Det utvecklade systemet kan följa en person genom en kontorsmiljö somsträcker sig över flera våningsplan. Detta med enbart lite information om personensinitiala position. Systemet kan även återhämta sig från situationer däralgoritmen divergerar vilket ökar systemets pålitlighet på lång sikt.

Localization and mapping are two of the most central tasks when it comes toautonomous robots. It has often been performed using expensive, accurate sensorsbut the fast development of consumer electronics has made similar sensorsavailable at a more affordable price. In this master thesis a TurtleBot\texttrademark\, robot and a MicrosoftKinect\texttrademark\, camera are used to perform Simultaneous Localization AndMapping, SLAM. The thesis presents modifications to an already existing opensource SLAM algorithm. The original algorithm, based on visual odometry, isextended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter,EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF areimplemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set isrecorded in an ordinary office room which constitutes an environment with manylandmarks. The other set is recorded in a conference room where one of the wallsis flat and white. This gives a partially sparse featured environment. The result by providing additional sensor information is a more robust algorithm.Periods without credible visual information does not make the algorithm lose itstrack and the algorithm can thus be used in a larger variety of environmentsincluding such where the possibility to extract landmarks is low. The resultalso shows that the visual odometry can cancel out drift introduced bywheel odometry and gyro sensors.

To evaluate the traffic state over time and space, several models can be used. A typical model for estimating the state of the traffic for a stretch of road or a road network is the cell transmission model, which is a form of state space model. This kind of model typically needs to be calibrated since the different roads have different properties. This thesis will present a calibration framework for the velocity based cell transmission model, the CTM-v. The cell transmission model for velocity is a discrete time dynamical system that can model the evolution of the velocity field on highways. Such a model can be fused with an ensemble Kalman filter update algorithm for the purpose of velocity data assimilation. Indeed, enabling velocity data assimilation was the purpose for ever developing the model in the first place and it is an essential part of the Mobile Millennium research project. Therefore a systematic methodology for calibrating the cell transmission is needed. This thesis presents a framework for calibration of the velocity based cell transmission model that is combined with the ensemble Kalman filter. The framework consists of two separate methods, one is a statistical approach to calibration of the fundamental diagram. The other is a black box optimization method, a simplification of the complex method that can solve inequality constrained optimization problems with non-differentiable objective functions. Both of these methods are integrated with the existing system, yielding a calibration framework, in particular highways were stationary detectors are part of the infrastructure. The output produced by the above mentioned system is highly dependent on the values of its characterising parameters. Such parameters need to be calibrated so as to make the model a valid representation of reality. Model calibration and validation is a process of its own, most often tailored for the researchers models and purposes. The combination of the two methods are tested in a suit of experiments for two separate highway models of Interstates 880 and 15, CA which are evaluated against travel time and space mean speed estimates given by Bluetooth detectors with an error between 7.4 and 13.4 % for the validation time periods depending on the parameter set and model.

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.