A gimbal is a pivoted device that decouples movements of a platform from its payload. The payload is a camera which must be stabilized to capture video without motion disturbances. A challenge with this type of gimbal is that a wide span of cameras with different sizes and weights can be used. The change of camera has significant effect on the dynamics of the gimbal and therefore the control system must be retuned. This tuning is inconvenient, especially for someone without knowledge of control engineering. This thesis reviews suitable methods to perform an automatic controller tuning directly on the gimbal's hardware.This tuning starts by exciting the system and then using data to estimate a model. This model is then used to control the gimbal, thus removing the need for manual tuning of the system. The foundation of this thesis is a physical model of the gimbal, derived through the Lagrange equation. The physical model has undetermined parameters such as inertias, centre of gravity and friction constants. System identification is used to determine these parameters. A problem discussed is how the system should be excited in order to achieve data with as much information as possible about the dynamics. This problem is approached by formulating an optimization problem that can be used find suitable trajectories. The identified model is then used to control the gimbal. Different methods for model based-control are discussed. By using a method called feedback linearisation all of the parameter-dependant dynamics of the gimbal can be compensated for. Apart from being independent of model parameters the new outer system is also decoupled and linear. A PID controller is used for feedback control of the outer system. The uncertainty of the feedback linearisation is analysed to find the effects of model errors.To assure robustness of the closed loop system a Lyapunov redesign controller is used to compensate for these model errors. Some experimental results are also presented. The quality of the estimated model is evaluated. Additionally, the reference tracking performance of the control system is tested and results reveal issues with the estimated model's performance.

The stringent regulations of emission levels from heavy duty vehicles create a demand for new methods for reducing harmful emissions from diesel engines. This thesis deals with the modelling of the nitrogen oxide (NOx) emissions from heavy duty vehicles using a selective catalyst as an aftertreatment system, utilising ammonia (NH3) for its reduction. The process of the selective catalytic reduction (SCR) is nonlinear, since the result of the chemical reactions involved depends on the load operating point and the temperature. The purpose of this thesis is to investigate different methods for nonlinear system identification of SCR systems with control applications in mind. The main focus of the thesis is on finding suitable techniques for effective NOx reduction without the need of over dosage of ammonia. By using data collected from a simulator together with real measured data, new black-box identification techniques are developed. Scaling and convergence properties of the proposed algorithms are analysed theoretically. Some of the resulting models are used for controller development using e.g. feedback linearisation techniques, followed by validation in a simulator environment. The benefits of nonlinear modelling and control of the SCR system are highlighted in a comparison with control based on linear models of the system. Further, a multiple model approach is investigated for simultaneous control of NOx and tailpipe ammonia. The results indicate an improvement in terms of ammonia slip reduction in comparison with models that do not take the ammonia slip into account. Another approach to NOx reduction is achieved by controlling the SCR temperature using techniques developed for LPV systems. The results indicate a reduction of the accumulated NOx.

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.