<p>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.</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>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.</p><p>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.</p><p>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.</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>This thesis has been conducted at Saab Bofors Dynamics AB. The purpose was to investigate if a non-linear missile model could be stabilized when the optimal control signal is computed considering constraints on the control input. This is particularly interesting because the missile is controlled with rudders that have physical bounds. This strategy is called Model Predictive Control. Simulations are conducted to compare this strategy with others; firstly simulations with step responses and secondly simulations when the missile is supposed to hit a moving target. The latter is performed to show that the missile can be stabilized in its whole area of operation. The simulations show that the controller indeed can stabilize the missile for the given scenarios. However, this control strategy does not show any obvious improvements in comparison with alternative ones.</p>

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.</p>

<p>Today’s diesel engines are complex with systems like VGT and EGR to be able to fulfil the stricter emission legislations and the demands on the fuel consumption. Controlling a system like this demands a sophisticated control system. Furthermore, the authorities demand on self diagnosis requires an equal sophisticated diagnosis system. These systems require good knowledge about the signals present in the system and how they affect each other.</p><p>One way to achieve this is to have a good model of the system and based on this calculate an observer. The observer is then used to estimate signals used for control and diagnosis. Advantages with an observer instead of using just sensors are that the sensor signals often are noisy and need to be filtered before they can be used. This causes time delay which further complicates the control and diagnosis systems. Other advantages are that sensors are expensive and that some engine quantities are hard to measure.</p><p>In this Master’s thesis a model of a Scania diesel engine is developed and an observer is calculated. Due to the non-linearities in the model the observer is based on a constant gain extended Kalman filter.</p>

<p>The longitudinal controller at DaimlerChrysler AG consists of two cascade controllers. The outer control loop contains the driver assistance functions such as speed limiter, cruise control, etc. The inner control loop consists of a PID-controller and an observer. The task of the observer is to estimate the part of the vehicle's acceleration caused by large disturbances, for example by a changed vehicle mass or the slope of the road.</p><p>As observer the Kalman filter is selected. It is the optimal filter when the process model is linear and the process noise and measurement noise can be modeled as Gaussian noise. In this Master's thesis the theory for the Kalman filter is presented and it is shown how to choose the filter parameters. Simulated annealing is a global optimization technique which can be used when autotuning, i.e., automatically find the optimal parameter settings. To be able to perform autotuning for the longitudinal controller one has to model the environment and driving situations.</p><p>In this Master's thesis it is verified that the parameter choice is a compromise between a fast but jerky, or a slow but smooth estimate. As the output from the Kalman filter is directly added to the control value for the engine and brakes, it is important that the output is smooth. It is shown that the Kalman filter implemented in the test vehicles today can be exchanged with a first-order lag function, without loss in performance. This makes the filter tuning easier, as there is only one parameter to choose.</p><p>Change detection is a method that can be used to detect large changes in the signal, and react accordingly - for example by making the filter faster. A filter using change detection is implemented and simulations show that it is possible to improve the estimate using this method. It is suggested to implement the change detection algorithm in a test vehicle and evaluate it further.</p>

<p>Scania CV AB is a large manufacturer of heavy duty trucks that, with an increasingly stricter emission legislation, have a rising demand for an effective On Board Diagnosis (OBD) system. One idea for improving the OBD system is to employ a model for the construction of an observer based diagnosis system. The proposal in this report is, because of a nonlinear model, to use a nonlinear filtering method for improving the needed state estimates. Two nonlinear filters are tested, the Particle Filter (PF) and the Extended Kalman Filter (EKF). The primary objective is to evaluate the use of the PF for Fault Detection and Isolation (FDI), and to compare the result against the use of the EKF.</p><p>With the information provided by the PF and the EKF, two residual based diagnosis systems and two likelihood based diagnosis systems are created. The results with the PF and the EKF are evaluated for both types of systems using real measurement data. It is shown that the four systems give approximately equal results for FDI with the exception that using the PF is more computational demanding than using the EKF. There are however some indications that the PF, due to the nonlinearities, could offer more if enough CPU time is available.</p>

<p>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.</p><p>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.</p><p>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.</p>