In today’s heavy-duty vehicles, autonomous systems are used to increase comfort and security by intervening or partially taking over the driver’s tasks. Examples of such existing systems are ABS (Anti Blocking System), EPS (Electronic Stability Program) and ACC (Autonomous Cruise Control). With access to accurate vehicle position these autonomous systems can be developed much further. One way to increase the accuracy is to use Real Time Kinematics (RTK) satellite navigation, which uses data from a fixed reference station (receiver with a known position) and carrier phase estimates, to improve the position estimations. The method has traditionally been used i.a. geodetic surveying, but is continuously expanding to other areas. In this thesis RTK is evaluated, using the open source program RTKLIB 2.4.1 and one of EUREFs reference stations, placed in Mårtsbo outside of Gävle. The best RTK results gave a RMS positioning error of 0.67m in the northern direction and 0.32m in the eastern direction. Overall, the RTKLIB solution did not provide any sufficient improvement over that of the standard absolute positioning method, but it is on par with results obtained by others using RTK open source solutions. The RTK solution needs to be investigated further and recommendations are given on how such an investigation could be performed.

Due to the rapid growth of wireless multimedia service, new features such as improved spectral efficiency, latency and increased peak throughput are standardized in the current GSM/EDGE system. Higher order modulations and symbol rates are defined in Enhanced General Packet Radio Service phase 2 (EGPRS2) to achieve better throughput. However, the computational complexity in the traditional receiver can be very high when increased modulation orders are applied; additionally, the system becomes more sensitive to the impairments at an increased symbol rate. It is therefore desirable to have a less complex and more robust demodulator. Recently, a new study item which introduces the multi-carrier technique was proposed in the 3rd Generation Partnership Project (3GPP) standardization. Based on the channel partitioning using the Discrete Fourier Transform (DFT), a simple equalizer can be used, which greatly reduces the computational complexity on receiver, meanwhile achieves good throughput and robustness against impairments. In this thesis, another channel partitioning method by means of the Discrete Cosine Transform (DCT) is studied. Transmitter and receiver algorithms are developed, including a pre-filter designed at the receiver to facilitate the channel diagonalization. The link level performance is evaluated by means of simulations, under different test scenarios. The system’s robustness against impairments and peak-to-average ratio (PAR) reduction are also evaluated, and compared with a system based on the DFT pre-coding. From the simulations, the conclusions can be drawn that in this implementation, the DFT precoded EGPRS2 outperforms the DCT pre-coded EGPRS2 in all scenarios. The DCT pre-coded system also shows worse robustness against impairments and higher peak-to-average ratio reduction loss in throughput. The impact of pre-filter design on the DCT pre-coded system has also been analyzed, and it shows that there is a tradeoff between achieving good symmetrization, and preserving channel information in the frequency domain.

Event-triggered control is a recent approach in control theory which aims at reducing the communication load in networked control systems by adapting the communication among the components to the current needs. In more detail, the information exchange over the feedback link only takes place if certain event conditions, that guarantee a desired control performance, are satisfied. This thesis analyzes the consequences of actuator saturation on the stability of the event-triggered control loop. Based on linear matrix inequalities, stability criteria have been derived which can be used to determine regions in the state space that guarantee a stable behavior. Furthermore, the existence of a lower bound on the minimum inter-event time is shown. Due to integrator windup, actuator saturation may severely degrade the performance of the event-triggered closed-loop system. In order to overcome this problem, the stability criteria have been extended by incorporating a static anti-windup structure. Finally, the effects of transmission delays in the feedback link are analyzed and a procedure to deal with their consequences is proposed. The results are illustrated by simulations and by experiments with a wirelessly controlled first-order tank system.

The primary source for pedestrian navigation is the well known Global Positioning System. However, for applications including pedestrians walking in urban or indoor environments the GPS is not always reliable since the signal often is corrupted or completely blocked. A solution to this problem is to make a fusion between the GPS and an Inertial Navigation System (INS) that uses sensors attached to the pedestrian for positioning. The sensor platform consists of a tri-axial accelerometer, gyroscope and magnetometer. In this thesis, a dead reckoning approach is proposed for the INS, which means that the travelled distance is obtained by counting steps and multiplying with step length. Three parts of the dead reckoning system are investigated; step detection, motion classification and step length estimation. A method for step detection is proposed, which is based on peak/valley detection in the vertical acceleration. Each step is then classified based on the motion performed; forward, backward or sideways walk. The classification is made by extracting relevant features from the sensors, such as correlations between sensor signals. Two different classifiers are investigated; the first makes a decision by looking directly on the extracted features using simple logical operations, while the second uses a statistical approach based on a Hidden Markov Model. The step length is modelled as a function of sensor data, and two diffrerent functions are investigated. A method for on-line estimation of the step length function parameters is proposed, enabling the system to learn the pedestrian's step length when the GPS is active. The proposed algorithms were implemented in Simulink R and evaluated using real data collected from field tests. The results indicated an accuracy of around 2 % of the travelled distance for 8 minutes of walking and running without GPS.

Abstract By the first of November 2013 there will be a new legislation introduced in the European Union to improve road safety, which is being developed by the United Nations Economic Commission for Europe. The new legislation will make it mandatory for all heavy-vehicles to have an Advanced Emergency Braking System (AEB system). This means that they need to be equipped with a system which has the capability of braking the vehicle if it is about to crash into the rear of a vehicle in front, provided that the driver does not make any action. This M aster’sthesis, which was initiated by Scania CV AB, covers the topic of making sure the AEB system never interferes with the drivers impending evasive maneuver. It presents three possible methods for identifying situations for when the drivers control should not be interfered. The requirement of a method, which has been provided by Scania CV AB, is that it should rely on minimal sensor input and require little computational resources. One of the three proposed methods meets these requirements well and has thus been further developed into an implementable on-board function. This method has been tested in simulation software as well as in an actual truck with promising results; the method correctly classified all the evasive maneuvers it was tested with. It can however be noted that there are situations which gets erroneously classified as an evasive maneuver, although none which have proved to be relevant since the AEB system is not operating in these situations.

This thesis is a study of model predictive control of water levels and flows in a water system. The water system studied includes five lakes and six dams that are regulated manually by sluice-gates. The water is used in the papermaking process at Holmen Paper Mill in Hallstavik. The aim of this thesis is to find out how to control the water system when all dams are automated and to minimize the discharge of water from the system without risking production stops due to water shortage. To fulfil the aim, a simulation is made during a dry period with low amount of rain. The simulation is then compared to the same period but when the system was manually controlled. In this thesis two models of the water system are constructed, a simple linear model and a more complex non-linear model. In the linear model the channels between the lakes are assumed to be delays of water flow. In the non-linear model the same channels are described by Saint Venant equations of changes of flow and Manning’s equation on how water flow and the cross-section of a channel are related. In both models, the lakes are modelled as the change in volume with respect to time due to inflow to and outflow from the lake. The non-linear model is verified against measured water levels, flows, sluice-gate heights and precipitation to ensure that the model describes the water system well enough. The linear model is used in the model predictive controller to calculate the optimal outflow from the dams. The optimal outflows are then converted into optimum gate heights in the dams, which in turn are used as input to the non-linear model. The non-linear model is used to simulate the water system. The results from the simulation show that the control of the water system can significantly be improved. The conclusion of this thesis is that a lot more water can be saved when the system is automated and that the water levels in the lakes can be kept more stable with respect to a set reference level. The recommendation if only one dam is to be controlled initially is to start with the dam at Närdingen.

The first main part of this thesis presents a novel distributed event-based control strategy for the synchronization of a network consisting of N identical dynamical linear systems. The problem statement can be interpreted as a generalization of a classical event-based consensus problem. Each system updates its control signals according to some triggering conditions based on local information only. Starting with the event-based synchronization with state feedback two different approaches are derived. The trigger functions of the first approach depend on the transfered system states, while the trigger conditions of the second approach depend on the control inputs. The advantages (or otherwise) of both approaches and implementations are discussed. Furthermore, we extend the problem setup to the synchronization with a dynamic output feedback coupling and transfer the both event-based methods to this problem. The novel proposed approaches yields to the general results for the event-based synchronization for linear systems. In the second smaller part we present a new method for the distributed event-based synchronization of Kuramoto oscillators. We assume a uniform Kuramoto model of N all-to-all connected oscillators with different natural frequencies. Throughout the report, simulation results validate and illustrate the proposed theoretical results.

With an unstable oil market and increasing prices, the focus has never been higher on reducing the fuel consumption costs. Using a navigation system that presents the fastest route, can today help a driver to reduce the fuel consumption by reducing the risk of not finding the target. A further development of such system is eco-routing: route optimization with regard to fuel consumption. Eco-routing is today being introduced to the market by several passenger car manufacturers. An essential part of an eco-routing system is estimation of fuel consumption. The requirements of a fuel consumption model for trucks is studied in this thesis. Two estimation methods were evaluated and compared, one of the methods was estimating fuel consumption using look up tables consisting fuel consumption data. The other method was estimation doing real time calculations with a fuel consumption model. With a simplified real time model the modelling error was approximately lower than 5% for rural and highway driving, however for city driving the accuracy was significantly reduced. Ecorouting simulations for a truck showed a mean saving of 8.5% for a set of long haulage routes.

The possibility to detect reliably human brain signals by small sensors can have substantial impact in healthcare, training, and rehabilitation. This Master the- sis studies Electroencephalography (EEG) wireless sensors, and the properties of their signals. The main goal is to investigate the problem of data interpre- tation accuracy. The measurements provided by small wireless EEG sensors show high variability and high noises, which makes it dicult to interpret the brain signals. The analysis is further exacerbated by the diculty in statistical modeling of these signals. This work presents an attempt to a simple statistical modeling of brain signals. Then, based on such a modeling, an optimal data fusion rule of sensors readings is proposed so to reach a high accuracy in the signal's interpretation. An experimental implementation of the data fusion by real EEG wireless sensors is developed. The experimental results show that the fusion rule provides an error probability of nearly 25% in detecting correctly brain signals. It is concluded that substantial improvements have still to be done to understand the statistical properties of signals and develop optimal decision rules for the detection.

This master thesis was carried out at Scania CV AB, and was proposed in order to explore the possibilities to modernize the present test rig for low pressure parts in the fuel systems in trucks. The rig at the present is hard to maneuver and it is also problematic to do accurate measurements, thus maintain decent repeatability when testing a series of parts. It is also almost impossible to regulate flow and pressure. By integrating a computer for the controlling of the rig and logging of data many of the problems can be solved. During the time of the thesis a conceptual plan, hardware suggestions and complete software was designed to meet today’s requirements for the testing of low pressure parts. Models of flow and pressure have also been made in order to prove what control strategy is the best one to use. The software has been implemented and tested for flow control and is shown to work satisfactory.