In the automatic manual transmission system, Opticruise, the choice of gear is based on several parameters such as road incline, driving resistance and vehicle mass. Many different mass estimations are made during driving. A final vehicle mass is then used to determine the current gear. Construction vehicles are often not equipped with air suspension and can therefore not estimate the vehicle mass when standing still. If that sort of vehicle is reloaded while standing still an incorrect mass estimation will be used and as an effect of that also a wrong gear. The solution of this problem is divided into two parts: first to detect the reload and then to estimate the new mass. An accelerometer will be used to detect a reload before start, because it reacts on the gradient change of the vehicle. A force equation will also be used to detect a reload, but only after the start, because it needs access to the vehicle acceleration to make the calculation. After a reload has been detected a new mass can be estimated. The estimation is based on previous vehicle mass estimated during driving. The new mass will be reasonable, if the assumption that the vehicle is mostly driving empty or fully loaded is true, and if it is possible to determine whether the vehicle becomes lighter or heavier at the reload.

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Estimation of the biological profile from as many skeletal elements as possible is a necessity in both forensic and bioarchaeological contexts; this includes non-standard aspects of the biological profile, such as body mass index (BMI). BMI is a measure that allows for understanding of the composition of an individual and is traditionally divided into four groups: underweight, normal weight, overweight, and obese. BMI estimation incorporates both estimation of stature and body mass. The estimation of stature from skeletal elements is commonly included into the standard biological profile but the estimation of body mass needs to be further statistically validated to be consistently included. The bones of the foot, specifically the first metatarsal, may have the ability to estimate BMI given an allometric relationship to stature and the mechanical relationship to body mass. There are two commonly used methods for stature estimation, the anatomical method and the regression method. The anatomical method takes into account all of the skeletal elements that contribute to stature while the regression method relies on the allometric relationship between a skeletal element and living stature. A correlation between the metrics of the first metatarsal and living stature has been observed, and proposed as a method for valid stature estimation from the boney foot (Byers et al., 1989). Body mass estimation from skeletal elements relies on two theoretical frameworks: the morphometric and the mechanical approaches. The morphometric approach relies on the size relationship of the individual to body mass; the basic relationship between volume, density, and weight allows for body mass estimation. The body is thought of as a cylinder, and in order to understand the volume of this cylinder the diameter is needed. A commonly used proxy for this in the human body is skeletal bi-iliac breadth from rearticulated pelvic girdle. The mechanical method of body mass estimation relies on the ideas of biomechanical bone remodeling; the elements of the skeleton that are under higher forces, including weight, will remodel to minimize stress. A commonly used metric for the mechanical method of body mass estimation is the diameter of the head of the femur. The foot experiences nearly the entire weight force of the individual at any point in the gait cycle and is subject to the biomechanical remodeling that this force would induce. Therefore, the application of the mechanical framework for body mass estimation could stand true for the elements of the foot. The morphometric and mechanical approaches have been validated against one another on a large, geographically disparate population (Auerbach and Ruff, 2004), but have yet to be validated on a sample of known body mass. DeGroote and Humphrey (2011) test the ability of the first metatarsal to estimate femoral head diameter, body mass, and femoral length. The estimated femoral head diameter from the first metatarsal is used to estimate body mass via the morphometric approach and the femoral length is used to estimate living stature. The authors find that body mass and stature estimation methods from more commonly used skeletal elements compared well with the methods developed from the first metatarsal. This study examines 388 `White' individuals from the William M. Bass donated skeletal collection to test the reliability of the body mass estimates from femoral head diameter and bi-iliac breadth, stature from maximum femoral length, and body mass and stature from the metrics of the first metatarsal. This sample included individuals from all four of the BMI classes. This study finds that all of the skeletal indicators compare well with one another; there is no statistical difference in the stature estimates from the first metatarsal and the maximum length of the femur, and there is no statistical between all three of the body mass estimation methods. When compared to the forensic estimates of stature neither of the tested methods had statistical difference. Conversely, when the body mass estimates are compared to forensic body mass there was a statistical difference and when further investigated the most difference in the body mass estimates was in the extremes of body mass (the underweight and obese categories). These findings indicate that the estimation of stature from both the maximum femoral length and the metrics of the metatarsal are accurate methods. Furthermore, the estimation of body mass is accurate when the individual is in the middle range of the BMI spectrum while these methods for outlying individuals are inaccurate. These findings have implications for the application of stature and body mass estimation in the fields of bioarchaeology, forensic anthropology, and paleoanthropology.

Unmanned Aerial Vehicles (UAV) control has become a very important point of scientific study. The control design challenges of a UAV make it one of the most researched areas in modern control applications. This thesis specifically chooses the Quadrotor as the UAV platform. Considering the quadrotor has 4 rotors and 6 degrees of freedom, it is an underactuated system and is dynamically unstable that has to be stabilized by a suitable control algorithm in order to operate autonomously. This thesis focuses on the quaternion representation of the quadrotor system dynamics and develops an adaptive control for its trajectory tracking problem. The control design uses the certainty equivalence principle where adaptive tracking controls are designed separately for each of the translational and rotational subsystems. With this approach, the success of the outer loop translational control relies on the fast convergence of the inner loop rotational control in order to guarantee the system’s stability while achieving the tracking objective. For the translational subsystem in the outer loop, a modified geometric control technique is considered with an adaptive component for the estimation of the uncertain mass of the quadrotor. For the rotational subsystem in the inner loop a backstepping based control design is adopted due to its systematic design and intuitive approach. An adaptive component is further integrated with it to estimate the integrated components of the uncertain Moment of Inertia matrix and other constant parameters in the system dynamics to guarantee the stability of the inner loop system while achieving the tracking objective. Furthermore, a complete backstepping control design methodology is presented which overcomes the issues of certainty equivalence principle where the inner loop needs to execute significantly faster than the outer loop to stabilize the system.

This Master's thesis presents a method for on-line estimation of vehicle mass and road grade using Kalman filter. Many control strategies aiming for better fuel economy, drivability and safety in today's vehicles rely on precise vehicle operating information. In this context, vehicle mass and road grade are crucial parameters. The method is based on an extended Kalman filter (EKF) and a longitudinal vehicle model. The main advantage of this method is its applicability on drivelines with continuous power output during gear shifts and cost effectiveness compared to hardware solutions. The performance has been tested on both simulated data and on real measurement data, collected with a truck on road. Two estimators were developed; one estimates both vehicle mass and road grade and the other estimates only vehicle mass using an inclination sensor as an additional measurement. Tests of the former estimator demonstrate that a reliable mass estimate with less than 5 % error is often achievable within 5 minutes of driving. Furthermore, the root mean square error of the grade estimate is often within 0.5°. Tests of the latter estimator show that this is more accurate and robust than the former estimator with a mass error often within 2 %. A sensitivity analysis shows that the former estimator is fairly robust towards minor modelling errors. Also, an observability analysis shows under which circumstances simultaneous vehicle mass and road grade is possible.

One of the causes of work-related Musculoskeletal Disorders (MSDs) is the manual handling of heavy objects. To reduce the risk of such injuries, workers are instructed to follow general guidelines on how to lift and carry objects depending on their mass. Current ergonomic assessments using wearable sensors can differentiate correct from incorrect body postures but are limited. Being able to estimate the mass of an object during ergonomic assessment would be a great improvement. This work investigates a combination of Inertial Measurement Units (IMUs) and Electromyography (EMG) sensors for offline estimation of an object’s mass for different movements. 10 participants performed 26 lifting and carrying trials with loads from 0 to 19 kg, while wearing a 17IMU motion capture system and EMG sensors on both biceps brachii and both erector spinae. Two methods were considered to estimate the carried mass: (1) supervised machine learning and (2) musculoskeletal modeling. First, the data was used to select features, train, and compare regression models. The lowest Mean Squared Error (MSE) for 10-fold cross-validation for lifting and carrying combined was 5.8113 for a Gaussian Process Regression (GPR) model with an exponential kernel function. Then, a MSE of 4.42 and a Mean Absolute Error (MAE) of 1.63 kg were obtained also with a GPR for Leave-One-Subject-Out Cross-Validation (LOSOCV) only for lifting and frontal carrying trials. For the same trials, the upper-extremity musculoskeletal model, scaled to each participant, estimated the mass with a MSE of 1.78 and a MAE of 0.95 kg. The study was restricted to lifting and frontal carrying, but the combination of the two technologies showed great potential for object mass estimation.

An accurate estimate of the mass of a passenger vehicle is important for several safety systems and environmental aspects. In this thesis, an algorithm for estimating the mass of a passenger vehicle using the recursive least squares methodis presented. The algorithm is based on a physical model of the vehicle and is designed to be able to run in real-time onboard a vehicle and uses the wheel torque signal calculated in the electrical control unit in the engine. Therefore no estimation of the powertrain is needed. This is one contribution that distinguishes this thesis from previous work on the same topic, which has used the engine torque. The benefit of this is that no estimation of the dynamics in the powertrain is needed. The drawback of using this method is that the algorithm is dependenton the accuracy of the estimation done in the engine electrical control unit. Two different versions of the recursive least squares method (RLS) have been developed - one with a single forgetting factor and one with two forgetting factors. The estimation performance of the two versions are compared on several different real-world driving scenarios, which include driving on country roads, highways, and city roads, and different loads in the vehicle. The algorithm with a single forgetting factor estimates the mass with an average error for all tests of 4.42% and the algorithm with multiple forgetting factors estimates the mass with an average error of 4.15 %, which is in line with state-of-the-art algorithms that are presented in other studies. In a sensitivity analysis, it is shown that the algorithms are robust to changes in the drag coefficient. The single forgetting factor algorithm is robust to changes in the rolling resistance coefficient whereas the multiple forgetting factor algorithm needs the rolling resistance coefficient to be estimated with fairly good accuracy. Both versions of the algorithm need to know the wheel radius with an accuracy of 90 %. The results show that the algorithms estimate the mass accurately for all three different driving scenarios and estimate highway roads best with an average error of 2.83 % and 2.69 % for the single forgetting factor algorithm and the multiple forgetting factor algorithm, respectively. The results indicate it is possible to use either algorithm in a real-world scenario, where the choice of which algorithm depends on sought-after robustness.

The goal of this work was to calculate the mass in the bucket of a underground loaderfrom pressure data in the cylinders. Three different approaches to using a Kalman filter toestimate the loaded mass were tried and evaluated in MATLAB simulations. Of these, twogave promising results when tried on real data and a combination of the two methods issuggested as solution to the problem. The filters required a model for the mechanics of themachine which was also derived.

In dieser Projektarbeit geht es um die Abschätzung von Massen der Hauptbaugruppen großer ziviler Verkehrsflugzeuge (MTOM > 40 t), sowie um die Abschätzung der Betriebsleermasse. Die Projektarbeit analysiert die 2013 im Luftfahrttechnischen Handbuch (LTH) erschienene Massenschätzmethode MA 401 12-01 B von F. Dorbarth und vergleicht diese Methode mit anderen früher veröffentlichten Methoden, die von Fernandes da Moura bereits 2001 analysiert wurden. Für die Analyse werden ausgewählte Flugzeugmuster (A320-200, A330-200, A340-300 und B737-200) und deren tatsächliche Massen der Hauptbaugruppen sowie Betriebsleermassen genutzt. Die Abweichungen zwischen den berechneten und den tatsächlichen Massen werden für jede Methode in Diagrammen veranschaulicht. Es zeigt sich dabei, dass die Massenschätzmethode aus dem Luftfahrttechnischen Handbuch nur geringe Abweichungen im Vergleich zu den tatsächlichen Massen aufweist. Damit werden die eigenen Angaben zur Genauigkeit der LTH-Methode bestätigt. Die Abweichungen sind geringer als bei älteren und generelleren Methoden wie sie von Fernandes da Moura untersucht wurden. Dies entspricht der Erwartung, dass eine neuere Methode, die auf Flugzeuge einer bestimmten Art beschränkt ist, auch genauere Ergebnisse liefert. Insgesamt hat sich die LTH-Methode als übersichtliche und hinreichend genaue Methode zur Massenabschätzung im frühen Flugzeugentwurf erwiesen. Die Abweichungen lagen in der Regel unter 5 % und nur in Ausnahmefällen wurde eine Abweichung von 10 % überschritten.

<p>The weight of a heavy duty vehicle plays an important role when dealing with different control systems. Examples of control units in a truck that need this parameter are the ones used to control the brakes, the engine and the gearbox. An accurate estimation of the weight leads not only to a more fuel efficient and safer transport, but also assures the driver that current law limits are not exceeded. The weight can be estimated with pretty good accuracy if the truck is equipped with air suspension. In trucks that lack this type of suspension other methods are used to estimate the weight. At present these methods are inaccurate. In this thesis a new method where the weight is to be estimated through frequency analysis of the truck's driveline is developed and evaluated.</p>