Real-time interval type-2 fuzzy control of an unmanned aerial vehicle with flexible cable-connected payload

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers

The Complete Reference (Volume 4)

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.info:eu-repo/semantics/publishedVersio

REAL-TIME ERROR DETECTION AND CORRECTION FOR ROBUST OPERATION OF AUTONOMOUS SYSTEMS USING ENCODED STATE CHECKS

The objective of the proposed research is to develop methodologies, support algorithms and software-hardware infrastructure for detection, diagnosis, and correction of failures for actuators, sensors and control software in linear and nonlinear state variable systems with the help of multiple checks employed in the system. This objective is motivated by the proliferation of autonomous sense-and-control real-time systems, such as intelligent robots and self-driven cars which must maintain a minimum level of performance in the presence of electro-mechanical degradation of system-level components in the field as well as external attacks in the form of transient errors. A key focus is on rapid recovery from the effects of such anomalies and impairments with minimal impact on system performance while maintaining low implementation overhead as opposed to traditional schemes for recovery that rely on duplication or triplication. On-line detection, diagnosis and correction techniques are investigated and rely on analysis of system under test response signatures to real-time stimulus. For on-line error detection and diagnosis, linear and nonlinear state space encodings of the system under test are used and specific properties of the codes, as well as machine learning model based approaches were used are analyzed in real-time. Recovery is initiated by copying check model values to correct error for sensor and control software malfunction, and by redesigning the controller parameter on-the-fly for actuators to restore system performance. Future challenges that need to be addressed include viability studies of the proposed techniques on mobile autonomous system in distributed setting as well as application to systems with soft as well as hard real-time performance constraints.Ph.D

Reference Governors for MIMO Systems and Preview Control: Theory, Algorithms, and Practical Applications

The Reference Governor (RG) is a methodology based on predictive control for constraint management of pre-stablized closed-loop systems. This problem is motivated by the fact that control systems are usually subject to physical restrictions, hardware protection, and safety and efficiency considerations. The goal of RG is to optimize the tracking performance while ensuring that the constraints are satisfied. Due to structural limitations of RG, however, these requirements are difficult to meet for Multi-Input Multi-Output (MIMO) systems or systems with preview information. Hence, in this dissertation, three extensions of RG for constraint management of these classes of systems are developed. The first approach aims to solve constraint management problem for linear MIMO systems based on decoupling the input-output dynamics, followed by the deployment of a bank of RGs for each decoupled channel, namely Decoupled Reference Governor (DRG). This idea was originally developed in my previous work based on transfer function decoupling, namely DRG-tf. This dissertation improves the design of DRG-tf, analyzes the transient performance of DRG-tf, and extends the DRG formula to state space representations. The second scheme, which is called Preview Reference Governor, extends the applicability of RG to systems incorporated with the preview information of the reference and disturbance signals. The third subject focuses on enforcing constraints on nonlinear MIMO systems. To achieve this goal, three different methods are established. In the first approach, which is referred to as the Nonlinear Decoupled Reference Governor (NL-DRG), instead of employing the Maximal Admissible set and using the decoupling methods as the DRG does, numerical simulations are used to compute the constraint-admissible setpoints. Given the extensive numerical simulations required to implement NL-DRG, the second approach, namely Modified RG (M-RG), is proposed to reduce the computational burden of NL-DRG. This solution consists of the sequential application of different RGs based on linear prediction models, each robustified to account for the worst-case linearization error as well as coupling behavior. Due to this robustification, however, M-RG may lead to a conservative response. To lower the computation time of NL-DRG while improving the performance of M-RG, the third approach, which is referred to as Neural Network DRG (NN-DRG), is proposed. The main idea behinds NN-DRG is to approximate the input-output mapping of NL-DRG with a well-trained NN model. Afterwards, a Quadratic Program is solved to augment the results of NN such that the constraints are satisfied at the next timestep. Additionally, motivated by the broad utilization of quadcopter drones and the necessity to impose constraints on the angles and angle rates of drones, the simulation and experimental results of the proposed nonlinear RG-based methods on a real quadcopter are demonstrated

Flight controller synthesis via deep reinforcement learning

Traditional control methods are inadequate in many deployment settings involving autonomous control of Cyber-Physical Systems (CPS). In such settings, CPS controllers must operate and respond to unpredictable interactions, conditions, or failure modes. Dealing with such unpredictability requires the use of executive and cognitive control functions that allow for planning and reasoning. Motivated by the sport of drone racing, this dissertation addresses these concerns for state-of-the-art flight control by investigating the use of deep artificial neural networks to bring essential elements of higher-level cognition to bear on the design, implementation, deployment, and evaluation of low level (attitude) flight controllers. First, this thesis presents a feasibility analyses and results which confirm that neural networks, trained via reinforcement learning, are more accurate than traditional control methods used by commercial uncrewed aerial vehicles (UAVs) for attitude control. Second, armed with these results, this thesis reports on the development and release of an open source, full solution stack for building neuro-flight controllers. This stack consists of a tuning framework for implementing training environments (GymFC) and firmware for the world’s first neural network supported flight controller (Neuroflight). GymFC’s novel approach fuses together the digital twinning paradigm with flight control training to provide seamless transfer to hardware. Third, to transfer models synthesized by GymFC to hardware, this thesis reports on the toolchain that has been released for compiling neural networks into Neuroflight, which can be flashed to off-the-shelf microcontrollers. This toolchain includes detailed procedures for constructing a multicopter digital twin to allow the research and development community to synthesize flight controllers unique to their own aircraft. Finally, this thesis examines alternative reward system functions as well as changes to the software environment to bridge the gap between simulation and real world deployment environments. The design, evaluation, and experimental work summarized in this thesis demonstrates that deep reinforcement learning is able to be leveraged for the design and implementation of neural network controllers capable not only of maintaining stable flight, but also precision aerobatic maneuvers in real world settings. As such, this work provides a foundation for developing the next generation of flight control systems

Control-Theoretical Perspective in Feedback-Based Systems Testing

Self-Adaptive Systems (SAS) and Cyber-Physical Systems (CPS) have received significant attention in recent computer engineering research. This is due to their ability to improve the level of autonomy of engineering artefacts. In both cases, this autonomy increase is achieved through feedback. Feedback is the iteration of sens- ing and actuation to respectively acquire knowledge about the current state of said artefacts and steer them toward a desired state or behaviour. In this thesis we dis- cuss the challenges that the introduction of feedback poses on the verification and validation process for such systems, more specifically, on their testing. We highlight three types of new challenges with respect to traditional software testing: alteration of testing input and output definition, and intertwining of components with different nature. Said challenges affect the ways we can define different elements of the test- ing process: coverage criteria, testing set-ups, test-case generation strategies, and oracles in the testing process. This thesis consists of a collection of three papers and contributes to the definition of each of the mentioned testing elements. In terms of coverage criteria for SAS, Paper I proposes the casting of the testing problem, to a semi-infinite optimisation problem. This allows to leverage the Scenario Theory from the field of robust control, and provide a worst-case probabilistic bound on a given performance metric of the system under test. For what concerns the definition of testing set-ups for control-based CPS, Paper II investigates the implications of the use of different abstractions (i.e., the use of implemented or emulated compo- nents) on the significance of the testing. The paper provides evidence that confutes the common assumption present in previous literature on the existence of a hierar- chy among commonly used testing set-ups. Finally, regarding the test-case gener- ation and oracle definition, Paper III defines the problem of stress testing control- based CPS software. We contribute to the generation and identification of stress test cases for such software by proposing a novel test case parametrisation. Leveraging the proposed parametrisation we define metamorphic relations on the expected be- haviour of the system under test. We use said relations for the development of stress testing approach and sanity checks on the testing results

Autonomous High-Precision Landing on a Unmanned Surface Vehicle

THE MAIN GOAL OF THIS THESIS IS THE DEVELOPMENT OF AN AUTONOMOUS HIGH-PRECISION LANDING SYSTEM OF AN UAV IN AN AUTONOMOUS BOATIn this dissertation, a collaborative method for Multi Rotor Vertical Takeoff and Landing (MR-VTOL) Unmanned Aerial Vehicle (UAV)s’ autonomous landing is presented. The majority of common UAV autonomous landing systems adopt an approach in which the UAV scans the landing zone for a predetermined pattern, establishes relative positions, and uses those positions to execute the landing. These techniques have some shortcomings, such as extensive processing being carried out by the UAV itself and requires a lot of computational power. The fact that most of these techniques only work while the UAV is already flying at a low altitude, since the pattern’s elements must be plainly visible to the UAV’s camera, creates an additional issue. An RGB camera that is positioned in the landing zone and pointed up at the sky is the foundation of the methodology described throughout this dissertation. Convolutional Neural Networks and Inverse Kinematics approaches can be used to isolate and analyse the distinctive motion patterns the UAV presents because the sky is a very static and homogeneous environment. Following realtime visual analysis, a terrestrial or maritime robotic system can transmit orders to the UAV. The ultimate result is a model-free technique, or one that is not based on established patterns, that can help the UAV perform its landing manoeuvre. The method is trustworthy enough to be used independently or in conjunction with more established techniques to create a system that is more robust. The object detection neural network approach was able to detect the UAV in 91,57% of the assessed frames with a tracking error under 8%, according to experimental simulation findings derived from a dataset comprising three different films. Also created was a high-level position relative control system that makes use of the idea of an approach zone to the helipad. Every potential three-dimensional point within the zone corresponds to a UAV velocity command with a certain orientation and magnitude. The control system worked flawlessly to conduct the UAV’s landing within 6 cm of the target during testing in a simulated setting.Nesta dissertação, é apresentado um método de colaboração para a aterragem autónoma de Unmanned Aerial Vehicle (UAV)Multi Rotor Vertical Takeoff and Landing (MR-VTOL). A maioria dos sistemas de aterragem autónoma de UAV comuns adopta uma abordagem em que o UAV varre a zona de aterragem em busca de um padrão pré-determinado, estabelece posições relativas, e utiliza essas posições para executar a aterragem. Estas técnicas têm algumas deficiências, tais como o processamento extensivo a ser efectuado pelo próprio UAV e requer muita potência computacional. O facto de a maioria destas técnicas só funcionar enquanto o UAV já está a voar a baixa altitude, uma vez que os elementos do padrão devem ser claramente visíveis para a câmara do UAV, cria um problema adicional. Uma câmara RGB posicionada na zona de aterragem e apontada para o céu é a base da metodologia descrita ao longo desta dissertação. As Redes Neurais Convolucionais e as abordagens da Cinemática Inversa podem ser utilizadas para isolar e analisar os padrões de movimento distintos que o UAV apresenta, porque o céu é um ambiente muito estático e homogéneo. Após análise visual em tempo real, um sistema robótico terrestre ou marítimo pode transmitir ordens para o UAV. O resultado final é uma técnica sem modelo, ou que não se baseia em padrões estabelecidos, que pode ajudar o UAV a realizar a sua manobra de aterragem. O método é suficientemente fiável para ser utilizado independentemente ou em conjunto com técnicas mais estabelecidas para criar um sistema que seja mais robusto. A abordagem da rede neural de detecção de objectos foi capaz de detectar o UAV em 91,57% dos fotogramas avaliados com um erro de rastreio inferior a 8%, de acordo com resultados de simulação experimental derivados de um conjunto de dados composto por três filmes diferentes. Também foi criado um sistema de controlo relativo de posição de alto nível que faz uso da ideia de uma zona de aproximação ao heliporto. Cada ponto tridimensional potencial dentro da zona corresponde a um comando de velocidade do UAV com uma certa orientação e magnitude. O sistema de controlo funcionou sem falhas para conduzir a aterragem do UAV dentro de 6 cm do alvo durante os testes num cenário simulado. Traduzido com a versão gratuita do tradutor - www.DeepL.com/Translato

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader