Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

SDR for Physical Layer Authentication

Wireless networks and devices are easy and useful solutions nowadays, regardless of the context in which they are implemented. However, it is in the broadcast nature of wireless networks that some vulnerabilities arise. To protect against these vulnerabilities, encryp- tion and authentication methods are commonly used. However, such methods come at the expense of their own complexity, requiring high enough computational power to solve, and introducing latency. To try to reduce the complexity of the conventional ways of user authentication, this work has studied mechanisms to implement reliable authentication at the physical layer, analyzing the various devices signal characteristics. To achieve this analysis, the GNU Radio platform was used to process incoming signals and extract the necessary features. Given the open source nature of GNU Radio, this provides a customiz- able and low-cost solution to signal processing and feature extraction. This research uses the GNU Radio to implement a feature extraction solution and constructs a feature vector with size 1 × 95. This thesis studies the extracted features of eleven IEEE 802.15.4 devices in regards to their separability and proposes a solution for feature reduction. The feature vectors are passed through a Random Forest and a Deep Neural Network (DNN) classifier, achieving accuracies as high as 99% for short distance communication.Redes e dispositivos sem fio são implementações úteis e fáceis de realizar atualmente, independentemente do contexto em que são desenvolvidas. No entanto, é na natureza de difusão destas redes que surgem algumas vulnerabilidades. Métodos de criptografia e autenticação são usualmente utilizados para proteger contra essas vulnerabilidades. No entanto, esses métodos apresentam uma complexidade inerente, necessitando de poder computacional e introduzindo latência. Para tentar reduzir a complexidade das formas convencionais de autenticação de utilizadores das redes, esta dissertação estudou me- canismos para implementar uma autenticação fiável na camada física, analisando as ca- racterísticas dos sinais dos diversos dispositivos que utilizam a rede. Para realizar esta análise, a plataforma GNU Radio foi utilizada para processar sinais recebidos e extrair as características necessárias. Dada a natureza de código aberto do GNU Radio, é possível desenvolver uma solução customizável e de baixo custo. Esta dissertação utiliza o GNU Radio para implementar uma solução de extração de características e constrói um vetor de características de tamanho 1×95. Esta dissertação estuda as características extraídas de onze dispositivos IEEE 802.15.4 em relação à separabilidade destas e propõe uma solução para redução de características. Os vetores são passados por um classificador de Florestas Aleatórias (Random Forest) e um classificador de Redes Neurais Profundas, atingindo precisões de até 99% para comunicação a curta distância

Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical.The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field.Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research.Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining.In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost.In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and make sure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms

Robust fault tolerant control of induction motor system

Research into fault tolerant control (FTC, a set of techniques that are developed to increase plant availability and reduce the risk of safety hazards) for induction motors is motivated by practical concerns including the need for enhanced reliability, improved maintenance operations and reduced cost. Its aim is to prevent that simple faults develop into serious failure. Although, the subject of induction motor control is well known, the main topics in the literature are concerned with scalar and vector control and structural stability. However, induction machines experience various fault scenarios and to meet the above requirements FTC strategies based on existing or more advanced control methods become desirable. Some earlier studies on FTC have addressed particular problems of 3-phase sensor current/voltage FTC, torque FTC, etc. However, the development of these methods lacks a more general understanding of the overall problem of FTC for an induction motor based on a true fault classification of possible fault types.In order to develop a more general approach to FTC for induction motors, i.e. not just designing specific control approaches for individual induction motor fault scenarios, this thesis has carried out a systematic research on induction motor systems considering the various faults that can typically be present, having either “additive” fault or “multiplicative” effects on the system dynamics, according to whether the faults are sensor or actuator (additive fault) types or component or motor faults (multiplicative fault) types.To achieve the required objectives, an active approach to FTC is used, making use of fault estimation (FE, an approach that determine the magnitude of a fault signal online) and fault compensation. This approach of FTC/FE considers an integration of the electrical and mechanical dynamics, initially using adaptive and/or sliding mode observers, Linear Parameter Varying (LPV, in which nonlinear systems are locally decomposed into several linear systems scheduled by varying parameters) and then using back-stepping control combined with observer/estimation methods for handling certain forms of nonlinearity.In conclusion, the thesis proposed an integrated research of induction motor FTC/FE with the consideration of different types of faults and different types of uncertainties, and validated the approaches through simulations and experiments