Currently, encryption is part of daily life, from commercial to industrial applications. Secure, long-distance communication is vital to the safe and reliable operation of industrial feedback control. Utilising public networks as a medium is often cost-effective at the risk of a security breach. Current industrial feedback control systems generally utilise end-to-end encryption to communicate control signals and gains securely. Data has to be decrypted for processing. Homomorphic encryption allows for manipulation of encrypted data. This eliminates the need for decryption to update controller states and calculating control effort. Partially Homomorphic Encryption supports either multiplication or addition of encrypted values, whereas Fully Homomorphic Encryption allows for both. Besides being flexible, Fully Homomorphic Encryption schemes are thought to be quantum safe. Unfortunately, Fully Homomorphic Encryption schemes are computationally expensive limiting practical applications. This thesis presents an enhanced version of the of the popular Fully Homomorphic Encryption scheme by Gentry. The encryption scheme is enhanced through the introduction of three alterations. New notation is introduced that streamlines its description. The main functions that compose the encryption scheme are all replaced with analytical equivalents. The so called reduced cipher is introduced. Rewriting the encryption scheme using the improved notation, analytical functions and reduced cipher leads to a more computationally and memory efficient implementation. The alterations make the encryption more suitable for implementation on Field Programmable Gate Arrays which decreases compute time. Such an implementation is presented and used to demonstrate the efficacy of the enhanced encryption scheme.

As autonomous driving is a popular and ever growing field of research, real world experiments provide a required manner of testing. In this thesis a driving research platform is developed, with a focus on platooning using visual messaging. These visual messages are conveyed using LED matrices. This thesis proposes two methods of LED matrix detection using YOLOv2, one using a sliding window, and one using the entire image. Furthermore two ways of distance estimation are proposed, one using the centers of the estimation bounding boxes and one using the used camera proprietary toolbox depth map. Results from an online experiment show best results from the depth map based depth estimation. The LED matrix detection using a sliding window gave generally dependable results in different environments, at the cost of being computationally demanding. The detection using the entire image provided less consistent results, but was significantly less computationally demanding. In a second offline experiment using a preannotated validation dataset as groundtruth all LED matrices were detected for all detectors. The SqueezeNet based YOLOv2 detector using a sliding window had the best results between tested detectors, with the highest intersection over union between detection and groundtruth.

Unmanned vehicles are a vital topic in today’s science and technology field. The safety problem of unmanned vehicles has been paid more attention from researchers. People are continually developing new control technologies, making the auxiliary driving or control of vehicles more accurate and reliable. Before designing a reliable controller, researchers need to obtain an accurate model of the vehicle system. However, the vehicle is a complex system, and various vehicle parameters are difficult to obtain by direct sensor measurement. In addition, there are deviations between the actual vehicle and the simulation model. At this time, it is necessary to make system identification to obtain reliable vehicle parameters and models. In this paper, vehicle simulation is carried out based on the CarSim C-Class model under the environment of CarSim vehicle simulation software. The model includes tire, suspension, steering, driver, and other subsystems. This platform can simulate a vehicle closed to the real one. The vehicle model can be controlled through Simulink and output the real-time data of various variables of the vehicle system. Then, the data required for identification and validation can be obtained through the joint simulation of CarSim and Simulink. By comparing different vehicle and tire models, different identification data sets and different algorithms, this paper summarizes the advantages and disadvantages of different choices and their applicability. First, the comparison between the bicycle model and the four wheels vehicle model is implemented. The Interior-point algorithm was used to identify the two models under different control data sets. The results of parameter validation and vehicle validation are analyzed. Then, under the same control data set, the four wheels model is selected for the comparative experiment between different algorithms. The first comparison with the Interior-point algorithm is the Unscented Kalman Filter (UKF) and its improved method Particle Swarm Optimization-Unscented Kalman Filter (PSO-UKF). The Magic Formula tire model was then identified by Genetic Algorithm (GA) algorithm, which compares with the Dugoff tire model. Each model and algorithm has its suitable scenarios. Also different data sets lead to various result. The analysis and application suggestions of different algorithms, data sets and models will be given in the end.

This thesis report is focused on the design of a guidance and control system that is able to minimize the deviation from the desired impact point for a firing range of 1 kilometer, given a 30mm spinning gun-launched projectile with a novel actuator design. This actuator is fixed on the projectile and offers a single force that can only be switched on or off.

In order to test the effectiveness of the guidance, navigation and control (GNC) system, an accurate model for both the projectile and the proposed actuator are constructed. These models will serve as a substitute for testing with working prototypes. The model for the projectile dynamics is constructed based on existing nonlinear 6-DOF rigid-body models that are widely-used and validated. A new, simplified second order model that approximates the dynamics of the actuator, based on available measurements, is constructed.

The overall structure of the GNC loop is defined, with the guidance method of choice being the pre-calculation of a reference trajectory towards the intended target. This method serves to alleviate under-actuation and computation time problems. One of the most important contributions of the thesis is made with the description of a method for transforming the input from a single binary input signal towards two continuous virtual input forces. This method uses a discretization of the rolling motion of the projectile, such that an optimization can be done which results in a transformation from the binary on/off signal to two virtual force inputs. These two virtual force represent the steering forces in the directions perpendicular to the forwards motion that would have the same effect as the binary on/off signal if directly applied on the projectile.

The restructuring of the input allows for the use of PD-controllers to track the pre-defined trajectory. Using a grid-search method, the controller gains are found that best satisfy the design goal of minimizing the dispersion, which is defined by the sum of the mean error and the standard deviation of the impact points.

Analysis of the integrated solution shows that the proposed solution with the input transformation and the PD-controller is able to significantly reduce the projectile dispersion from standard deviations of about 50 cm to just a few cm. The exact performance depends on the frequency of the actuator and the spin rate of the projectile. The best performance is reached with high spin rates and actuator frequencies high enough to match these spin rates.