A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

Kalman filtering is a well-established method for fusing sensor data in order to accuratelyestimate unknown variables. Recently, the unscented Kalman filter (UKF) has beenused due to its ability to propagate the first and second moments of the probability distribution of an estimated state through a non-linear transformation. The design of ageneric algorithm which implements this filter occupies the first part of this thesis. The generality and functionality of the filter were tested on a toy example and the results are within machine accuracy when compared to those of an equivalent C++ implementation.Application of this filter to the attitude navigation problem becomes non-trivial when coupled to quaternions. Challenges present include the non-commutation of rotations and the dimensionality difference between quaternions and the degrees of freedom of the motion. The second part of this thesis deals with the formulation of the UKF in the quaternion space. This was achieved by implementing an error-state formulation of the process model, bounding estimation in the infinitesimal space and thus de-coupling rotations from non-commutation and bridging the dimensionality discrepancy of quaternions and their respective covariances.The attitude navigation algorithm was then tested using an IMU and a magnetometer.Results show a bounded estimation error which settles to around 1 degree. A detailed look of the filter mechanization process was also presented showing expected behavior for estimation of the initial attitude with error tolerance of 1 mdeg. The structure and design of the proposed formulation allows for trivially incorporating other sensors inthe estimation process and more intricate modelling of the stochastic processes present,potentially leading to greater estimation accuracy. / Kalman filtrering är en vältablerad metod for att sammanväga sensordata för att erhålla noggranna estimat av okända variabler. Nyligen har den typ av kalman filter som kallas unscented Kalman filter (UKF) ökat i populäritet pa grund av dess förmåga att propagera de första och andra momenten för sannolikhetsfördelningen för ett estimera tillstånd genom en ickelinjär transformation. Designen av en generisk algoritm som implementerar denna typ av filter upptar den första delen av denna avhandling. Generaliteten och funktionaliteten för detta filter testades på ett minimalt exempel och resultaten var identiska med de för en ekvivalent C++-implementation till den noggrannhet som tillåts av den nita maskinprecisionen. Användandet av detta filter för attitydnavigering blir icke-trivialt när det anvands forkvaternioner. De utmaningar som uppstar inkluderar att rotationer inte kommuterar och att de finns en skillnad i dimensionalitet mellan kvaternioner och antalet frihetsgrader i rörelsen. Den andra delen av denna avhandling behandlar formuleringen av ett UKF för ett tillstånd som inkluderar en kvaternion. Detta gjordes genom att implementera en så kallad error state-formulering av processmodellen, vilken begränsar estimeringen till ett innitesimalt tillstånd och därigenom undviker problemen med att kvaternionmultiplikation inte kommuterar och överbryggar skillnaden i dimensionalitet hos kvaternioner och deras motsvarande vinkelosäkerheter.Attitydnavigeringen testades sedan med hjälp av en IMU och en magnetometer.Resultaten visade ett begränsat estimeringsfel som ställer in sig kring 1 grad. Strukturen och designen av den föreslagna formuleringen möjliggör på ett rattframt satt tillägg av andra sensorer i estimeringsprocessen och mer detaljerad modellering av de stokastiska processerna, vilket potentiellt leder till högre estimering noggrannhet.

his study explores clamp force estimation using Unscented Kalman Filtering (UKF) in torque-controlled tightening scenarios with various velocity profiles. Previous research has explored the impact of velocity levels on target torque and clamping force, but only using hand-held tools. Prior research is extended by implementing UKF in a fixed setup, using the QST42, to remove user errors. Four strategies, Continuous Drive, TurboTight, Accelerating Tightening, and Paused Tightening, are analyzed using error and quality factor metrics. In Continuous Drive, both hand-held and fixed rigshave mean errors of approximately 4.09% and 4.14%, with quality factors of 88.38% and 97.72%.UKF adapts well in TurboTight, with mean errors of 3.50% (hand-held) and 5.23% (fixed rigs), and quality factors of 93.02% and 94.44%, respectively. Dynamic strategies like Accelerating Tightening- yield higher mean errors (10.33%) and quality factors (94.86%), while Paused Tightening results in a mean error of 5.17% and a quality factor of 76.86%. Tailoring UKF calibration is crucial for accuracy. Overall, this research underscores the close correlation between UKF’s performance and the dynamics of the tightening strategy. The implications extend to industrial applications, advocating for strategy-specific adjustments to enhance clamp force estimation accuracy. This study contributes to advancing UKF’s applicability in real-world scenarios, providing a foundational framework to enhance the accuracy and reliability of clamp force estimations. / Denna studie utforskar kraftuppskattning för klammer i momentkontrollerade åtdragnings-scenarier med olika hastighetsprofiler med hjälp av Unscented Kalman Filtering (UKF). Tidigare forskning har utforskat påverkan av hastighetsnivåer på målmoment och klämkraft, men endast med användning av handhållna verktyg. Tidigare forskning utökas genom att implementera UKF i en fast inställning, med QST42 verktyget, för att eliminera användarfel. Fyra strategier, Continuous Drive, TurboTight, Accelerating Tightening och Paused Tight-ening, analyseras med hjälp av fel- och kvalitetsfaktormetoder. I Continuous Drive har både handhållna och fixta åtdragningar medelvärdesfel på cirka 4,09% och 4,14%, med kvalitetsfaktorer på 88,38% och 97,72%. UKF anpassar sig väl i TurboTight, med medelvärdesfel på 3,50% (handhållna) och 5,23%(fixt rig) och kvalitetsfaktorer på 93,02% och 94,44%, respektive. Dynamiska strategier som Accelerating Tightening ger högre medelvärdesfel (10,33%) och kvalitetsfaktorer (94,86%), medan Paused Tightening resulterar i ett medelvärdesfel på 5,17% och en kvalitetsfaktor på 76,86%. Sammanfattningsvis understryker denna forskning den nära korrelationen mellan UKF:s prestanda och dynamiken i åtdragningsstrategin. Implikationerna sträcker sig till industriella tillämpningar och förespråkar strategispecifika justeringar för att förbättra noggrannheten i klämkraftsuppskattningen. Denna studie bidrar till att främja användningen av UKF i verkliga scenarier och tillhandahåller en grundläggande ram för att förbättra noggrannheten och tillförlitligheten i klämkraftsuppskattning.