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.

A critical requirement for safe autonomous driving is to have an accurate state estimate of thevehicle. One of the most ubiquitous yet reliable ways for this task is through the integrationof the onboard Inertial Navigation System (INS) and the Global Navigation Satellite System(GNSS). This integration can further be assisted through fusion of information from otheronboard sensors. On top of that, a ground vehicle enforces its own set of rules, through non-holonomic constraints, which along with other vehicle dynamics can aid the state estimation.In this project, a sequential probabilistic inference approach has been followed, that fusesthe high frequency, short term accurate INS estimates, with low frequency, drift free GPSobservations. The fusion of GPS and IMU has been sought through a modular asynchronousloosely coupled framework, capable of augmenting additional observation sources to facilitatethe state estimation and tracking process. Besides GPS and IMU, the applied strategy makesuse of wheel speed sensor measurements, nonholonomic constraints and online estimationof IMU sensor biases as well wheel speed scalling factor. Theses augmentations have beenshown to increase the robustness of the localization module, under periods of GPS outage.The Extended Kalman Filter (EKF) has seen extensive usage for such sensor fusion tasks,however, the performance can be limited due to the propagation of the covariance throughlinearization of the underlying non-linear model. The Unscented Kalman Filter (UKF) avoidsthe issue of linearization based on jacobians. Instead, it uses a carefully chosen set ofsample points in order to accurately map the probability distribution. Correspondingly, thesurrounding literature also indicates towards the UKF out performing EKF in such tasks.Therefore, the present thesis also seeks to evaluate these claims.The EKF and SRUKF (Square Root UKF) instances of the developed algorithm have beentested on real sensor logs, recorded from a Scania test vehicle. Under no GPS outage situation,the implemented localization algorithm performs within a position RMSE of 60cm.The robustness of the localization algorithm, to GPS outages, is evaluated by simulating0-90% lengths of GPS unavailability, during the estimation process. Additionally, to unfoldthe impact of parameters, the individual modules within the suggested framework wereisolated and analysed with respect to their contribution towards the algorithm’s localizationperformance.Out of all, the online estimation of IMU sensor biases proved to be critical for increasingthe robustness of the suggested localization algorithm to GPS shortage, especially for the EKF.In terms of the distinction, both the EKF and the SRUKF performed to similar capabilities,however, the UKF showed better results for higher levels of GPS cuts. / Ett kritiskt krav för säker autonom körning är att ha en korrekt tillståndsuppskattning avfordonet. Ett av de mest förekommande men ändå tillförlitliga sätten för denna uppgift ärgenom integrationen av det inbyggda tröghetsnavigationssystemet (INS) och med Satellitnavi-gation (GNSS). Denna integration kan ytterligare underlättas genom sammanslagning avinformation från andra sensorer ombord. Utöver det upprätthåller ett markfordon sin egenuppsättning regler, genom icke-holonomiska begränsningar, som tillsammans med annanfordonsdynamik kan hjälpa till vid tillståndsuppskattningen.I detta projekt har en sekventiell probabilistisk slutledning följts, som sammansmälterde högfrekventa, kortsiktiga exakta INS-uppskattningarna, med lågfrekventa, driftfria GPS-observationer. Sammanslagningen av GPS och IMU har sökts genom ett modulärt asynkrontlöst kopplat ramverk, som kan utökas med ytterligare observationskällor för att underlättatillståndsuppskattningen och spårningsprocessen. Förutom GPS och IMU använder dentillämpade strategin mätningar av hjulhastighetssensorer, icke-holonomiska begränsningaroch onlineuppskattning av IMU-sensorbias samt hjulhastighetsskalningsfaktor. Dessa tillägghar visat sig öka robustheten hos lokaliseringsmodulen under perioder utan GPS-signal.Extended Kalman Filter (EKF) har sett omfattande användning för sådana sensorfusionsup-pgifter, men prestandan kan begränsas på grund av spridningen av kovariansen genomlinearisering av den underliggande icke-linjära modellen. Unscented Kalman Filter (UKF)undviker frågan om linearisering baserad på jacobianer. Istället använder den en noggrantutvald uppsättning provpunkter för att korrekt kartlägga sannolikhetsfördelningen. På motsva-rande sätt indikerar den omgivande litteraturen också mot UKF att utföra EKF i sådanauppgifter. Därför försöker denna avhandling också utvärdera dessa påståenden.EKF- och SRUKF-instanserna (Square Root UKF) av den utvecklade algoritmen hartestats på sensorloggar, inspelade från ett Scania-testfordon. Utan GPS-avbrott presterar denimplementerade lokaliseringsalgoritmen inom en position RMSE på 60 cm.Robustheten hos lokaliseringsalgoritmen, vid GPS-avbrott, utvärderas genom att simulera0-90% längder av GPS-otillgänglighet under uppskattningsprocessen. Utöver det har deenskilda modulerna inom det föreslagna ramverket isolerats och analyserats med avseendepå deras bidrag till algoritmens lokaliseringsprestanda.Av allt visade sig onlineuppskattningen av IMU-sensorbiaser vara avgörande för att ökarobustheten hos den föreslagna lokaliseringsalgoritmen mot GPS-brist, särskilt för EKF. Närdet gäller distinktionen presterade både EKF och SRUKF med liknande förmåga, men UKFvisade bättre resultat vid längre perioder utan GPS-signal.

Deep Learning Networks (DLN) is a relatively new artificial intelligence algorithm that gained popularity quickly due to its unprecedented performance. One of the key elements for this success is DL’s ability to extract a high-level of information from large amounts of raw data. This ability comes at the cost of high computational and memory requirements for the training process. Estimation algorithms such as the Extended Kalman Filter (EKF) and the Smooth Variable Structure Filter (SVSF) are used in literature to train small Neural Networks. However, they have failed to scale well with deep networks due to their excessive requirements for computation and memory size. In this thesis the concept of using EKF and SVSF for DLN training is revisited. A New family of filters that are efficient in memory and computational requirements are proposed and their performance is evaluated against the state-of-the-art algorithms. The new filters show competitive performance to existing algorithms and do not require fine tuning. These new findings change the scientific community’s perception that estimation theory methods such as EKF and SVSF are not practical for their application to large networks. A second contribution from this research is the application of DLN to Fault Detection and Diagnosis. The findings indicate that DL can analyze complex sound and vibration signals in testing of automotive starters to successfully detect and diagnose faults with 97.6% success rates. This proves that DLN can automate end-of-line testing of starters and replace operators who manually listen to sound signals to detect any deviation. Use of DLN in end-of-line testing could lead to significant economic benefits in manufacturing operations. In addition to starters, another application considered is the use of DLN in monitoring of the State-Of-Charge (SOC) of batteries in electric cars. The use of DLN for improving the SOC prediction accuracy is discussed. / Thesis / Doctor of Science (PhD) / There are two main ideas discussed in this thesis, both are related to Deep Learning (DL). The first investigates the use of estimation theory in DL network training. Training DL networks is challenging as it requires large amounts of data and it is computationally demanding. The thesis discusses the use of estimation theory for training of DL networks and its utility in information extraction. The thesis also presents the application of DL networks in an end-of-line Fault Detection and Diagnosis system for complex automotive components. Failure of appropriately testing automotive components can lead to shipping faulty components that can harm a manufacturer’s reputation as well as potentially jeopardizing safety. In this thesis, DL is used to detect and analyze complex fault patterns of automotive starters, complemented by sound and vibration measurements.

Mechanical systems operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for quantifying the effects of such uncertainties on the system response. This work uses the polynomial chaos framework to develop new methodologies for the simulation, parameter estimation, and control of mechanical systems with uncertainty. This study has led to new computational approaches for parameter estimation in nonlinear mechanical systems. The first approach is a polynomial-chaos based Bayesian approach in which maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. The second approach is based on the Extended Kalman Filter (EKF). The error covariances needed for the EKF approach are computed from polynomial chaos expansions, and the EKF is used to update the polynomial chaos representation of the uncertain states and the uncertain parameters. The advantages and drawbacks of each method have been investigated. This study has demonstrated the effectiveness of the polynomial chaos approach for control systems analysis. For control system design the study has focused on the LQR problem when dealing with parametric uncertainties. The LQR problem was written as an optimality problem using Lagrange multipliers in an extended form associated with the polynomial chaos framework. The solution to the Hâ problem as well as the H2 problem can be seen as extensions of the LQR problem. This method might therefore have the potential of being a first step towards the development of computationally efficient numerical methods for Hâ design with parametric uncertainties. I would like to gratefully acknowledge the support provided for this work under NASA Grant NNL05AA18A. / Ph. D.

Yes / In this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closed-loop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper.

With the rising popularity of ROVs and other UV solutions, more robust and high performance controllers have become a necessity. A model of the ROV or UV can be a valuable tool during control synthesis. The main objective of this thesis was to use a model in design and development of controllers for an ROV. In this thesis, an ROV from Blue Robotics was used. The ROV was equipped with 6 thrusters placed such that the ROV was capable of moving in 6-DOFs. The ROV was further equipped with an IMU, two pressure sensors and a magnetometer. The ROV platform was further developed with EKF-based sensor fusion, a control system and manual control capabilities. To model the ROV, the framework of Fossen (2011) was used. The model was estimated using two different methods, the prediction-error method and an EKF-based method. Using the prediction-error method, it was found that the initial states of the quaternions had a large impact on the estimated parameters and the overall fit to validation data. A Kalman smoother was used to estimate the initial states. To circumvent the problems with the initial quaternions, an \abbrEKF was implemented to estimate the model parameters. The EKF estimator was less sensitive to deviations in the initial states and produced a better result than the prediction-error method. The resulting model was compared to validation data and described the angular velocities well with around 70 % fit. The estimated model was used to implement feedback linearisation which was used in conjunction with an attitude controller and an angular velocity controller. Furthermore, a depth controller was developed and tuned without the use of the model. Performance of the controllers was tested both in real tests and simulations. The angular velocity controller using feedback linearisation achieved good reference tracking. However, the attitude controller could not stabilise the system while using feedback linearisation. Both controllers' performance could be improved further by tuning the controllers' parameters during tests. The fact that the feedback linearisation made the ROV unstable, indicates that the attitude model is not good enough for use in feedback linearisation. To achieve stability, the magnitude of the parameters in the feedback linearisation were scaled down. The assumption that the ROV's center of rotation coincides with the placement of the ROV's center of gravity was presented as a possible source of error. In conclusion, good performance was achieved using the angular velocity controller. The ROV was easier to control with the angular velocity controller engaged compared to controlling it in open loop. More work is needed with the model to get acceptable performance from the attitude controller. Experiments to estimate the center of rotation and the center of gravity of the ROV may be helpful when further improving the model.

This thesis investigates and implements the sensor fusion necessary to autonomously reverse a full size truck and trailer system. This is done using a LiDAR mounted on the rear of the truck along with a RTK-GPS. It is shown that the relative angles between truck-dolly and dolly-trailer can be estimated, along with global position and global heading of the trailer. This is then implemented in one of Scania's test vehicles, giving it the ability to continuously estimate these states. A controller is then implemented, showing that the full scale system can be stabilised in reverse motion. The controller is tested both on a static reference path and a reference path received from a motion planner. In these tests, the controller is able to stabilise the system well, allowing the truck to do complex manoeuvres backwards. A small lateral tracking error is present, which needs to be further investigated.

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi- carrier modulation scheme, which has been adopted for several wireless stan- dards. Systems employing this scheme at the physical layer are sensitive to frequency offsets and that causes Inter Carrier Interference (ICI) and degra- dation in overall system performance of OFDM systems. In this thesis work, an investigation on impairments of OFDM systems will be carried out. Anal- ysis of previous schemes for cancellation of the ICI will be done and a scheme for estimating and compensating the frequency offset based on Unscented Ka- man Filter (UKF) and Extended Kaman Filter (EKF) will be implemented. Analysis on how the UKF improves the Signal to Noise Ratio (SNR); and how well it tracks the frequency offset estimation under Additive White Gaussian Noise (AWGN) channel and flat fading Rayleigh channel will be carried on.

Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficulties and convergence problems are often encountered. Here one has to estimate the quantities namely, the initial state error covariance matrix Po, measurement noise covariance matrix R, the process noise covariance matrix Q and the system parameter 0 and the present work deals with the above. Since the above problem is fairly involved we need to have a good reference solution. For this purpose we utilize the approach and results of Gemson who considered the above problem via the extended Kalman filter (EKF) route to compare the present results from the MMLE route. The EKF uses the unknown parameters as additional states unlike in MMLE which uses only the system states. Chapter 1 provides a brief historical perspective followed by parameter identification in the presence of process and measurement noises. The earlier formulations such as natural, innovation, combined, and adaptive approaches are discussed. Chapter 2 deals with the heuristic adaptive tuning of the Kalman filter parameters for the matrices Q and R by Myers and Tapley originally developed for state estimation problems involving satellite orbit estimation. It turns out that for parameter estimation problems apart from the above matrices even the choice of the initial covariance matrix Po is crucial for obtaining proper parameter estimates with a finite amount of data and for this purpose the inverse of the information matrix for Po is used. This is followed by a description of the original Controlled Random Search (CRS) of Price and its variant as implemented and used in the present work to estimate or tune Q, R, and 0 which is the aim of the present work. The above help the reader to appreciate the setting under which the present study has been carried out. Chapter 3 presents the results and the analysis of the estimation procedure adopted with respect to a specific case study of the lateral dynamics of an aircraft involving 15 unknown parameters. The reference results for the present work are the ones based on the approach of Gemson and Ananthasayanam (1998). The present work proceeds in two phases. In the first case (i) the EKF estimates for Po, Q, and R are used to obtain 0 and in the second case (ii) the estimate of Po and Q together with a reasonable choice of R are utilized to obtain 0 from the CRS algorithm. Thus one is able to assess the capability of the CRS to estimate only the unknown parameters. The next Chapter 4 presents the results of utilizing the CRS algorithm with R based on a reasonable choice and for Po from the inverse of the information matrix to estimate both Q and 0. This brings out the efficiency of MMLE with CRS algorithm in the estimation of unknown process noise characteristics and unknown parameters. Thus it demonstratesthofcdifficult Q can be estimated using CRS technique without the attendant difficulties of the earlier MMLE formulations in dealing with process noise. Chapter 5 discusses the - implementation of CRS to estimate the unknown measurement noise covariance matrix R together with the unknown 0 by utilizing the values of Po and Q obtained through EKF route. The effect of variation of R in the parameter estimation procedure is also highlighted in This Chapter. This Chapter explores the importance of Po in the estimation procedure. It establishes the importance of Po though most of the earlier works do not appear to have recognized such a feature. It turned out that the CRS algorithm does not converge when some arbitrary value of Po is chosen. It has to be necessarily obtained from a scouting pass of the EKF. Some sensitivity studies based on variations of Po shows its importance. Further studies shows the sequence of updates, the random nature of process and measurement noise effects, the deterministic nature of the parameter, play a critical role in the convergence of the algorithm. The last Chapter 6 presents the conclusions from the present work and suggestions for further work.

The thesis outlines the development and application of a few novel dynamic state estimation based methods for estimation of parameters of vibrating engineering structures. The study investigates strategies for data fusion from multiple tests of possibly different types and different sensor quantities through the introduction of a common pseudo-time parameter. These strategies have been developed within the framework of Kalman and particle filtering techniques. The proposed methods are applied to a suite of problems that includes laboratory and field studies with a primary focus on finite element model updating of bridge structures and vehicle structure interaction problems. The study also describes how finite element models residing in commercially available softwares can be made to communicate with database of measurements via a particle filtering algorithm developed on the Matlab platform. The thesis is divided into six chapters and an appendix. A review of literature on problems of structural system identification with emphasis on methods on dynamic state estimation techniques is presented in Chapter 1. The problem of system parameter idenfification when measurements originate from multiple tests and multiple sensors is considered in Chapter 2. and solution based on Neumann expansion of the structural static/dynamic stiffness matrix and Kalman filtering is proposed to tackle this problem. The question of decoupling the problem of parameter estimation from state estimation is also discussed. The avoidance of linearization of the stiffness matrix and solution of the parameter problems by using Monte Carlo filters is examined in Chapter 3. This also enables treatment of nonlinear structural mechanics problems. The proposed method is assessed using synthetic and laboratory measurement data. The problem of interfacing structural models residing in professional finite element analysis software with measured data via particle filtering algorithm developed on Matlab platform is considered in Chapter 4. Illustrative examples now cover laboratory studies on a beam structure and also filed studies on an existing multi-span masonry railway arch bridge. Identification of parameters of systems with strong nonlinearities, such, as a rectangular rubber sheet with a concentric hole, is also investigated. Studies on parameter identification in beam moving oscillator problem are reported in Chapter 5. The efficacy of particle filtering strategy in identifying parameters of this class of time varying system is demonstrated. A resume of contributions made and a few suggestions for further research are provided in Chapter 6. The appendix contains details of development of interfaces among finite element software(NISA), data base of measurements and particle filtering algorithm (developed on Matlab platform).