New on-board multipurpose architecture integrating modern estimation techniques for generalized GNSS based autonomous orbit navigation

This dissertation investigates a novel Multipurpose Earth Orbit Navigation System (MEONS) architecture aiming at providing a generalized GNSS based spacecraft orbit estimation kernel matching the modern navigation instance of enhanced flexibility with respect to multiple Space Service Volume (SSV) applications (Precise Orbit Determination for Earth Observation satellite, Low Thrust Low to High Autonomous Orbit Rising, formation flying relative navigation, Small Satellite Autonomous Orbit Acquisition). The possibility to address theoretical and operational solutions within a unified framework is a foundamental step for the implementation of a reusable and configurable high performance navigation capability on next generation platforms

Pilot in loop assessment of fault tolerant flight control schemes in a motion flight simulator

This research presents the pilot in the loop tests carried out in a Six-Degree of Freedom (6-DOF) motion flight simulator to evaluate failure detection, isolation and identification (FDII) schemes for an advanced F-15 aircraft. The objective behind this study is to leverage the capability of the flight simulator at West Virginia University (WVU) to carry out a performance assessment of neurally augmented control algorithms developed on a Matlab/Simulink RTM platform. The experimental setup features an interface setup of Gen-2 SimulinkRTM schemes with MOTUS Flight Simulator (MFS). The set up is a close substitute to a real flight and thus is helpful in evaluation of the schemes in a realistic manner. The graphics in X-plane is used to obtain visual cues and the motion platform is used to obtain motion cues in the simulator cockpit. The whole set-up enables the pilot to respond with a joystick in the advent of a failure as he would otherwise in a real flight. The pilot response in maintaining the mission profile is different for different neural network augmentations and thus an indication of performance comparison of these schemes. Secondly, FDII schemes are developed for a sensor and actuator failure using an adaptive threshold for cross-correlation coefficients of the angular rates of the aircraft. Failure detection, isolation and identification logic is formulated based on monitoring the cross-correlation parameters with their Floating Limiter (FL) bounds. The FDII scheme developed shows a good performance with desktop simulation because of no pilot activity but with a pilot in the loop significant cross-correlation of the rates occur and hence the scheme become more susceptible to wrongs FDII. In addition, the pilot might induce some coupling of the cross-correlation parameters between detection and identification time which may trigger false detections and may configure the controller differently based on incorrect detection. Thus it is necessary that FDII scheme accommodate real flight conditions. The performance of the FDII schemes is improved with a pilot in the loop by monitoring the cross-correlation parameters and fine tuning FDII algorithms for real situations. This study has set up an excellent example to effectively utilize the aural, visual and motion cues to create a higher level of simulation complexity in designing control algorithms

Autonomous Approach and Landing Algorithms for Unmanned Aerial Vehicles

In recent years, several research activities have been developed in order to increase the autonomy features in Unmanned Aerial Vehicles (UAVs), to substitute human pilots in dangerous missions or simply in order to execute specific tasks more efficiently and cheaply. In particular, a significant research effort has been devoted to achieve high automation in the landing phase, so as to allow the landing of an aircraft without human intervention, also in presence of severe environmental disturbances. The worldwide research community agrees with the opportunity of the dual use of UAVs (for both military and civil purposes), for this reason it is very important to make the UAVs and their autolanding systems compliant with the actual and future rules and with the procedures regarding autonomous flight in ATM (Air Traffic Management) airspace in addition to the typical military aims of minimizing fuel, space or other important parameters during each autonomous task. Developing autolanding systems with a desired level of reliability, accuracy and safety involves an evolution of all the subsystems related to the guide, navigation and control disciplines. The main drawbacks of the autolanding systems available at the state of art concern or the lack of adaptivity of the trajectory generation and tracking to unpredicted external events, such as varied environmental condition and unexpected threats to avoid, or the missed compliance with the guide lines imposed by certification authorities of the proposed technologies used to get the desired above mentioned adaptivity. During his PhD period the author contributed to the development of an autonomous approach and landing system considering all the indispensable functionalities like: mission automation logic, runway data managing, sensor fusion for optimal estimation of vehicle state, trajectory generation and tracking considering optimality criteria, health management algorithms. In particular the system addressed in this thesis is capable to perform a fully adaptive autonomous landing starting from any point of the three dimensional space. The main novel feature of this algorithm is that it generates on line, with a desired updating rate or at a specified event, the nominal trajectory for the aircraft, based on the actual state of the vehicle and on the desired state at touch down point. Main features of the autolanding system based on the implementation of the proposed algorithm are: on line trajectory re-planning in the landing phase, fully autonomy from remote pilot inputs, weakly instrumented landing runway (without ILS availability), ability to land starting from any point in the space and autonomous management of failures and/or adverse atmospheric conditions, decision-making logic evaluation for key-decisions regarding possible execution of altitude recovery manoeuvre based on the Differential GPS integrity signal and compatible with the functionalities made available by the future GNSS system. All the algorithms developed allow reducing computational tractability of trajectory generation and tracking problems so as to be suitable for real time implementation and to still obtain a feasible (for the vehicle) robust and adaptive trajectory for the UAV. All the activities related to the current study have been conducted at CIRA (Italian Aerospace Research Center) in the framework of the aeronautical TECVOL project whose aim is to develop innovative technologies for the autonomous flight. The autolanding system was developed by the TECVOL team and the author’s contribution to it will be outlined in the thesis. Effectiveness of proposed algorithms has been then evaluated in real flight experiments, using the aeronautical flying demonstrator available at CIRA

NASA LaRC Workshop on Guidance, Navigation, Controls, and Dynamics for Atmospheric Flight, 1993

This publication is a collection of materials presented at a NASA workshop on guidance, navigation, controls, and dynamics (GNC&D) for atmospheric flight. The workshop was held at the NASA Langley Research Center on March 18-19, 1993. The workshop presentations describe the status of current research in the GNC&D area at Langley over a broad spectrum of research branches. The workshop was organized in eight sessions: overviews, general, controls, military aircraft, dynamics, guidance, systems, and a panel discussion. A highlight of the workshop was the panel discussion which addressed the following issue: 'Direction of guidance, navigation, and controls research to ensure U.S. competitiveness and leadership in aerospace technologies.

Intelligent and High-Performance Behavior Design of Autonomous Systems via Learning, Optimization and Control

Nowadays, great societal demands have rapidly boosted the development of autonomous systems that densely interact with humans in many application domains, from manufacturing to transportation and from workplaces to daily lives. The shift from isolated working environments to human-dominated space requires autonomous systems to be empowered to handle not only environmental uncertainties such as external vibrations but also interaction uncertainties arising from human behavior which is in nature probabilistic, causal but not strictly rational, internally hierarchical and socially compliant.This dissertation is concerned with the design of intelligent and high-performance behavior of such autonomous systems, leveraging the strength from control, optimization, learning, and cognitive science. The work consists of two parts. In Part I, the problem of high-level hybrid human-machine behavior design is addressed. The goal is to achieve safe, efficient and human-like interaction with people. A framework based on the theory of mind, utility theories and imitation learning is proposed to efficiently represent and learn the complicated behavior of humans. Built upon that, machine behaviors at three different levels - the perceptual level, the reasoning level, and the action level - are designed via imitation learning, optimization, and online adaptation, allowing the system to interpret, reason and behave as human, particularly when a variety of uncertainties exist. Applications to autonomous driving are considered throughout Part I. Part II is concerned with the design of high-performance low-level individual machine behavior in the presence of model uncertainties and external disturbances. Advanced control laws based on adaptation, iterative learning and the internal structures of uncertainties/disturbances are developed to assure that the high-level interactive behaviors can be reliably executed. Applications on robot manipulators and high-precision motion systems are discussed in this part

Large space structures and systems in the space station era: A bibliography with indexes (supplement 03)

Bibliographies and abstracts are listed for 1221 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1991 and June 30, 1991. Topics covered include large space structures and systems, space stations, extravehicular activity, thermal environments and control, tethering, spacecraft power supplies, structural concepts and control systems, electronics, advanced materials, propulsion, policies and international cooperation, vibration and dynamic controls, robotics and remote operations, data and communication systems, electric power generation, space commercialization, orbital transfer, and human factors engineering

Control and Analysis for Sequential Information based on Machine Learning

Sequential information is crucial for real-world applications that are related to time, which is same with time-series being described by sequence data followed by temporal order and regular intervals. In this thesis, we consider four major tasks of sequential information that include sequential trend prediction, control strategy optimisation, visual-temporal interpolation and visual-semantic sequential alignment. We develop machine learning theories and provide state-of-the-art models for various real-world applications that involve sequential processes, including the industrial batch process, sequential video inpainting, and sequential visual-semantic image captioning. The ultimate goal is about designing a hybrid framework that can unify diverse sequential information analysis and control systems For industrial process, control algorithms rely on simulations to find the optimal control strategy. However, few machine learning techniques can control the process using raw data, although some works use ML to predict trends. Most control methods rely on amounts of previous experiences, and cannot execute future information to optimize the control strategy. To improve the effectiveness of the industrial process, we propose improved reinforcement learning approaches that can modify the control strategy. We also propose a hybrid reinforcement virtual learning approach to optimise the long-term control strategy. This approach creates a virtual space that interacts with reinforcement learning to predict a virtual strategy without conducting any real experiments, thereby improving and optimising control efficiency. For sequential visual information analysis, we propose a dual-fusion transformer model to tackle the sequential visual-temporal encoding in video inpainting tasks. Our framework includes a flow-guided transformer with dual attention fusion, and we observe that the sequential information is effectively processed, resulting in promising inpainting videos. Finally, we propose a cycle-based captioning model for the analysis of sequential visual-semantic information. This model augments data from two views to optimise caption generation from an image, overcoming new few-shot and zero-shot settings. The proposed model can generate more accurate and informative captions by leveraging sequential visual-semantic information. Overall, the thesis contributes to analysing and manipulating sequential information in multi-modal real-world applications. Our flexible framework design provides a unified theoretical foundation to deploy sequential information systems in distinctive application domains. Considering the diversity of challenges addressed in this thesis, we believe our technique paves the pathway towards versatile AI in the new era

Design, validation and application of wave-to-wire models for heaving point absorber wave energy converters

Ocean waves represent an untapped source of renewable energy which can significantly contribute to the energy transition towards a sustainable energy mix. Despite the significant potential of this energy source and the multiple solutions suggested for the extraction of energy from ocean waves, some of which have demonstrated to be technically viable, no commercial wave energy farm has yet been connected to the electricity grid. This means that none of the technologies suggested in the literature has achieved economic viability. In order to make wave energy converters economically viable, it is essential to accurately understand and evaluate the holistic behaviour and performance of wave energy converters, including all the different conversion stages from ocean waves to the electricity grid. This can be achieved through wave tank or open ocean testing campaigns, which are extremely expensive and, thus, can critically determine the financial sustainability of the developing organisation, due to the risk of such large investments. Therefore, precise mathematical models that consider all the important dynamics, losses and constraints of the different conversion stages (including wave-structure hydrodynamic interaction and power take-off system), known as wave-to-wire models, are crucial in the development of successful wave energy converters. Hence, a comprehensive literature review of the different mathematical approaches suggested for modelling the different conversion stages and existing wave-to-wire models is presented, defining the foundations of parsimonious wave-to-wire models and their potential applications. As opposed to other offshore applications, wave energy converters need to exaggerate their motion to maximise energy absorption from ocean waves, which breaks the assumption of small body motion upon which linear models are based. An extensive investigation on the suitability of linear models and the relevance of different nonlinear effects is carried out, where control conditions are shown to play an important role. Hence, a computationally efficient mathematical model that incorporates nonlinear Froude-Krylov forces and viscous effects is presented. In the case of the power take-off system, mathematical models for different hydraulic transmission system configurations and electric generator topologies are presented, where the main losses are included using specific loss models with parameters identified via manufacturers’ data. In order to gain confidence in the mathematical models, the models corresponding to the different conversion stages are validated separately against either high-fidelity well-established software or experimental results, showing very good agreement. The main objective of this thesis is the development of a comprehensive wave-to-wire model. This comprehensive wave-to-wire model is created by adequately combining the subsystems corresponding to the different components or conversion stages. However, time-step requirements vary significantly depending on the dynamics included in each subsystem. Hence, if the time-step required for capturing the fastest dynamics is used in all the subsystems, unnecessary computation is performed in the subsystems with slower dynamics. Therefore, a multi-rate time-integration scheme is implemented, meaning that each subsystem uses the sample period required to adequately capture the dynamics of the components included in that conversion stage, which significantly reduces the overall computational requirements. In addition, the relevance of using a high-fidelity comprehensive wave-to-wire model in accurately designing wave energy converters and assessing their capabilities is demonstrated. For example, energy maximising controllers based on excessively simplified mathematical models result in dramatic consequences, such as negative average generated power or situations where the device remains stuck at one of the end-stops of the power take-off system. Despite the reasonably high-fidelity of the results provided by this comprehensive wave-towire model, some applications require the highest possible fidelity level and have no limitation with respect to computational cost. Hence, the simulation platform HiFiWEC, which couples a numerical wave tank based on computational fluid dynamics to the high-fidelity power take-off model, is created. In contrast, low computational cost is the main requirement for other applications and, thus, a systematic complexity reduction approach is suggested in this thesis, significantly reducing the computational cost of the HiFiWEC platform, while retaining the adequate fidelity level for each application. Due to the relevance of the nonlinearity degree when evaluating the complexity of a mathematical model, two nonlinearity measures to quantify this nonlinearity degree are defined. Hence, wave-to-wire models specifically created for each application are generated via the systematic complexity reduction approach, which provide the adequate trade-off between computational cost and fidelity level required for each application

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering