The master thesis is focused on creation of a visualization environment that displays a movement of a vehicle. A connection of the virtual reality with Matlab/Simulink and its 3D Animation toolbox is realized. Basic parameters of the vehicle can be easily edited using application created in Matlab. Selected dynamic effects are rendered together with the movement of the vehicle. The visualization is managed by results of the simulation calculations or measurements.

This thesis deals with aerodynamic forces and their influence on body position and ride comfort due changes in wheel loads. Simulation was made in computer program ADAMS (MSC Software TM).

<div>Traction control (TC) systems have become quite common in on-road passenger vehicles in recent years. However, for vehicles in other applications, they are not as widely available.</div><div>This work presents a methodology for the proper design and implementation of a traction control system for heavy duty off-road machines, using a wheel loader as a reference vehicle.</div><div><br></div><div><div>A simulation model was developed, using standard vehicle dynamics constructs, including equations of motion and a description of the distribution of weight between the axles for different operating conditions. This model contains considerations for resistive forces acting on the machine implement, such as that generated by a work pile. The simulation also incorporates a detailed representation of the slip-friction characteristics between the vehicle tires and the road surface. One objective of this research was to model this interaction accurately, because the system traction behavior is dependent on it. Therefore, a series of tests was run using a state estimator to generate data on the slip-friction relationship at various ground conditions, and the results were incorporated into the simulation model. The dynamics of the machine braking system pressure were also modeled to give a more accurate description of the system response. The result is a mathematical model capable of accurately reproducing the behavior of the real-world system.</div></div><div><br></div><div><div>One of the primary goals of this work was the description of the traction control strategy itself, which should work as effectively and efficiently as possible. Several different aspects of the system were taken into consideration in generating this control structure. First, a relatively simple controller based on a PID control law was created. This controller was updated to account for peculiarities of the traction control system, as well as aspects like time delay. From there, more advanced controllers were created to address certain aspects of the system in greater detail. First, a self-tuning controller based on real-time optimization strategies was developed, to allow the controller to quickly adapt to changes in ground condition. Then, different nonlinear controllers were synthesized which were designed to address the theoretical behavior of the system. All of these controllers were simulated using the system model and then some were run in experiments to show their potential for improving system performance. To improve system efficiency, the machine drivetrain itself was also examined to develop a more efficient control algorithm. By designing a more efficient methodology, traction control congurations which had previously seen increases in fuel consumption of 16% were now able to actually reduce fuel usage by 2.6%.</div></div><div><br></div><div><div>Another main goal of this work was the development of a prototype system capable of implementing the formulated control strategies. The reference machine was modied so that the brakes could be controlled electronically and independently for implementation of the TC system. The vehicle was instrumented using a wide array of sensors, and estimation methodologies for accurately determining vehicle speed and implement forces were designed. The velocity estimator designed in this work is more accurate and more reliable than an industry standard sensor, which is important for traction control implementation. The implement force estimate was also quite accurate, achieving payload estimate errors of less than 2.5%, comparable to commercially-available measurement systems. This setup allowed for tests to be accurately compared, to assess the traction control performance.</div></div><div><br></div><div><div>With the objective of performing experiments on the traction control system, many tests were run to assess its capabilities in various situations. These tests included experiments for characterizing the vehicle behavior so that the simulation model could be updated to accurately reflect the physical machine performance. Another task for the experimental work was the generation of useful metrics for quantifying traction control performance. Laboratory experiments which were very controlled and repeatable were also run for generating data to improve the system model and for comparing traction control performance results side-byside. The test metrics proposed for these experiments provided for accurate, repeatable comparisons of pushing force, tire wear, and brake consumption. For each of these tests, the traction control system saw an increase in pushing force of at least 10% when compared with the stock machine, with certain operating conditions showing increases as high as 60%. Furthermore, every test case showed a decrease in wheel slip of at least 45% (up to 73% for some cases), which translates into increased tire longevity.</div></div><div><br></div><div><div>Other tests were conducted in the eld, designed to mimic the real-world operating conditions of the wheel loader. Various performance comparisons were made for different congurations in which traction control could provide potential benets. These included parameters for comparing overall vehicle performance in a typical truck loading cycle, such as tire wear, fuel consumption, and material moved per load. Initial results for this testing showed a positive result in terms of wheel slip reduction, but other performance parameters such as fuel consumption were negatively impacted. Therefore, the control structure was reexamined extensively and new methods were added to improve those results. The final control implementation saw a 12% reduction in tire slip, while also reducing fuel consumption by 2.6% compared to the stock system. These results show signicant potential for traction control as a technology for maximizing the performance output of construction machines.</div></div>

This thesis is concerned with model predictive control (MPC) within the field of autonomous driving. MPC requires a model of the system to be controlled. Since a vehicle is expected to handle a wide range of driving conditions, it is crucial that the model of the vehicle dynamics is able to account for this. Differences in road grip caused by snowy, icy or muddy roads change the driving dynamics and relying on a single model, based on ideal conditions, could possibly lead to dangerous behaviour. This work investigates the use of Gaussian processes for learning a model that can account for varying road friction coefficients. This model is incorporated as an extension to a nominal vehicle model. A double lane change scenario is considered and the aim is to learn a GP model of the disturbance based on previous driving experiences with a road friction coefficient of 0.4 and 0.6 performed with a regular MPC controller. The data is then used to train a GP model. The GPMPC controller is then compared with the regular MPC controller in the case of trajectory tracking. The results show that the obtained GP models in most cases correctly predict the model error in one prediction step. For multi-step predictions, the results vary more with some cases showing an improved prediction with a GP model compared to the nominal model. In all cases, the GPMPC controller gives a better trajectory tracking than the MPC controller while using less control input.

It is possible to obtain information about the stability conditions of a vehicle by observing and comparing existing signals involved in the rotational motion of the vehicle around the vertical axis. Accurate information about the current state of a vehicle is critical for the development and function of new active safety features in a vehicle. Therefore, the goal of this thesis is to create a new signal based on already existing signals from the vehicle electronic control unit for detecting understeering and oversteering of a vehicle. The signal should consider all the factors that affect the evaluation of the vehicle´s stability conditions. The results show that the developed signal can, in certain conditions, detect understeering and oversteering. Issues arise in situations such as banked curves or low-mu surfaces. In those cases, the signal is not fully able to describe the vehicle behavior.

No description available.

In autonomous racing there is a need to accurately localize the vehicle while simultaneously creating a map of the track. This information can be delivered to planning and control layers in order to achieve fully autonomous racing. The kinematic model is a commonly used motion model in feature-based SLAM. However, it is a poor representation of the vehicle when considering high lateral accelerations since the model is only based on trigonometric relationships. This Master’s Thesis investigates the consequence of using the kinematic model when undertaking demanding maneuvers; and if by switching to a dynamic model, which takes the tire forces into account, can improve the localization performance. An EKF-SLAM algorithm comprising the kinematic and dynamic model was implemented on a development platform. The pose estimation accuracy was compared using either model when subject to typical maneuvers in racing-scenarios. The results showed that the pose estimation accuracy was in general similar when using either of the vehicle models. When exposed to large slip angles, the implications of switching from a kinematic model to a dynamic model resulted in a significantly better pose estimation accuracy when driving in an unknown environment. However, switching to a dynamic model had little effect when driving in a known environment. The implications of the study suggest that, during the first lap of a racing track, the kinematic model should be switched to a dynamic model when subject to high lateral accelerations. For the consecutive laps, the choice of vehicle model has less impact. Keywords: SLAM, EKF-SLAM, Localization, Estimation, Vehicle Dynamics, Kinematic Model, Dynamic Model, Autonomous Racing / I autonom racing är det viktigt att kunna lokalisera fordonet med hög noggrannhet samtidigt som en karta över banan skapas. Den här informationen kan vidare bli hanterad av planerings- och reglersystem för att uppfylla autonom racing fullt ut. Den kinematiska modellen är en vanligt förekommande rörelsemodell i SLAM. Den är däremot en bristande representation av fordonet vid höga laterala accelerationer eftersom modellen enbart är baserad på trigonometriska samband. Det här masterarbetet undersöker den kinematiska modellens påverkan vid olika manövrar och huruvida den dynamiska modellen, som modellerar däckkrafterna, kan förbättra prestandan. En EKF-SLAM algorithm innehållande den kinematiska- och dynamiska modellen implementerades på en utvecklingsplattform. Estimeringsnoggrannheten av positionen och orienteringen jämfördes vid typiska manövrar för racingscenarier. Resultatet visade att estimeringsnoggrannheten av positionen och orienteringen var generellt sett lika vid användandet av antingen den kinematiska eller den dynamiska modellen. Implikationerna av att byta från den kinematiska modellen till den dynamiska modellen vid höga glidvinklar, resulterade i en signifikant bättre estimeringsnoggrannhet av positionen och orienteringen vid körning i en okänd miljö. Emellertid så var effekterna av att byta till en dynamisk modell insignifikanta vid körning i en känd miljö. Implikationerna av denna studie föreslår att under det första varvet av racingbanan byta från den kinematiska modellen till den dynamiska vid höga laterala accelerationer. Under kommande varv har valet av fordonsmodell mindre effekt. Nyckelord: SLAM, EKF-SLAM, lokalisering, estimering, fordonsmodellering, kinematisk modell, dynamisk modell, autonom racing.

Doctor of Philosophy / The control systems installed in modern vehicles lack crucial information regarding the interaction between the tires and the road surface. This knowledge gap significantly impacts the safety and control of the vehicle. Thus, to address this issue, this research introduces a novel fusion approach to estimate friction at the tire-road contact interface. This hybrid fusion friction estimation algorithm employs techniques like signal processing and machine learning, backed up by information from various vehicle and tire dynamics models, to develop algorithms that estimate the level of friction between the tire and the road. This fusion approach enables more precise estimations of the friction coefficient in both normal driving situations and scenarios involving sudden changes in speed or road conditions. Therefore, this research aids in enhancing vehicle safety and control by providing improved information about such tire-road interactions.

This thesis contains the design and simulation test results for the implementation of a new all-wheel drive (AWD) torque vectoring (TV) control system. A separate algorithm using standard control methods is included in this study for a comparison. The proposed controller was designed to be applied to an AWD independent drive electric vehicle, however the main concepts can be re-purposed for other vehicle drive train configurations. The purpose of the control system is to assist the driver in achieving a desired vehicle trajectory whilst also maintaining stability and control of the vehicle. This is accomplished by measuring various real time parameters of the vehicle and using this information as feedback for the control system to act on. The focus of this thesis resides on the controller. Hence, this study assumes perfect observation of feedback parameters, therefore some uncertainties are not accounted for. Using feedback parameters, the control system will manage wheel slip whilst simultaneously generating a torque around the center of gravity of the vehicle by applying a torque differential between the left and right wheels. The proposed TV algorithm is simulated in MATLAB/Simulink along with another separate TV algorithm for comparison. Both algorithms are comprised of two main parts: a slip ratio controller applied to each wheel individually and stability controller that manages yaw rate and side slip of the vehicle. The new algorithm leverages the super twisting algorithm for the slip ratio controller and uses a fusion of a linear quadratic regulator with the integral term of a super twisting algorithm to implement the yaw rate and side slip controller. The other algorithm used for comparison derives its implementation for the slip ratio controller and yaw rate and side slip controllers from simple and standard first order sliding mode control methods. Both control algorithms were tested in three different main tests: anti-lock braking, sine dwell (SD) steering, and constant steering angle (CSA) tests. To increase the comprehensive nature of the study, the SD and CSA tests were simulated at 3 speeds (30,50, and 80 mph) and the steering angle parameter was varied from 2 to 24 degrees in increments of 2. The result of this study proves that the proposed controller is a feasible option for use in theory. Simulated results show advantages and disadvantages of the new controller with respect to the standard comparison controller. Both controllers are also shown to provide positive impacts on the vehicle response under most test conditions.

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