A dual-mode strategy for performance-maximisation and resource-efficient CPS design

The emerging scenarios of cyber-physical systems (CPS), such as autonomous vehicles, require implementing complex functionality with limited resources, as well as high performances. This paper considers a common setup in which multiple control and non-control tasks share one processor, and proposes a dual-mode strategy. The control task switches between two sampling periods when rejecting (coping with) a disturbance. We create an optimisation framework looking for the switching sampling periods and time instants that maximise the control performance (indexed by settling time) and resource efficiency (indexed by the number of tasks that are schedulable on the processor). The latter objective is enabled with schedulability analysis tailored for the dual-mode model. Experimental results show that (i) given a set of tasks, the proposed strategy improves the control performances whilst retaining schedulability; and (ii) given requirements on the control performances, the proposed strategy is able to schedule more tasks

Maximising runway capacity by mid-term prediction of runway configuration and aircraft sequencing using machine learning

Maximising runway capacity is one of the essential measures to meet the growing traffic demand. Runway capacity maximisation is an open challenge in the literature due to a complex and non-linear interplay of many factors which are stochastic in nature such as wind, weather, and arrival and departure sequence of aircraft. However, an effective sequencing of arrivals and departures may condense the service time on runway, thereby generating opportunities for new landing or take-off slots, which may maximise the runway capacity. In addition, sequencing of arrivals and departures optimised for a predicted runway configuration, given the weather and wind conditions, may lead to maximising the runway capacity. First, I develop an optimisation method, using aircraft position shifting and path-planning, for aircraft sequencing for a single runway airport. The proposed method can provide an optimal aircraft sequence, for both arrivals and departures, such that it minimises the total inter-arrival and departure times and, consequently, maximises the runway throughput. The proposed method implements several arrival/departure sequencing strategies, i.e., constraint position-shifting with one, two and N-positions, and First Come First Serve (FCFS) in order to obtain an optimal sequence (i.e., a sequence with the lowest process time). The novelty of the sequencing model is to incorporate the Standard Terminal Arrival Routes (STAR) for path planning and sequencing of arriving aircraft at Final Approach Fix (FAF) and departing aircraft sequence at the runway threshold. The simulation result demonstrates that the model can increase up to 15% of the runway capacity compared with the commonly used aircraft sequencing technique (i.e., FCFS). Second, a runway configuration (i.e., a set of runways active in a specific period in a multi-runaway system) plays a vital role in determining runway capacity. Thus, I developed an evolutionary computation (Cooperative Co-evolutionary with Genetic Algorithm) algorithm for determining which runway configuration is most suitable for processing a given optimal aircraft sequence (arrival-departure), such that the runway capacity is maximised in a multi-runway system. The proposed algorithm models and uses Runway Configuration Capacity Envelopes (RCCEs) which defines arrival and departure throughputs. RCCE helps in identifying the unique capacity constraints based on which runways are used, for example, arrivals/departures or both. In the proposed evolutionary algorithm (CCoGA), the runway configuration and aircraft sequence are modelled as two species which interacts and evolve co-operatively to yield the best populations (combination) for maximising runway capacity. The fitness function for the optimal sequence species is to reduce the total process time for a given runway configuration, while fitness function for the runway configuration species is to maximises the total capacity for the given optimal sequence. The simulation results show that CCoGA can provide trade-off solutions with multiple runway configurations, for a given arrival-departure sequence, which can lead to capacity maximisation. Third, the weather conditions at an airport play a major role in determining the runway configuration which then has a significant impact on its runway capacity. Typically, aircraft take-off and landing operations use the runways which are most closely aligned with the wind direction, speed and other factors (e.g., cloud ceiling, visibility). However, selecting a runway configuration is a challenging task because it requires not only wind/weather (current and predicted) conditions but also the arrival/departure sequence (active and anticipated) at an airport. Predicting a suitable runway configuration under the operating conditions and a given traffic distribution may be useful for maximising the runway capacity. To achieve that, I first propose a Machine Learning (ML) model for predicting a suitable runway configuration given wind/weather and arrival/departure sequence. The simulation results demonstrate the accuracy of the prediction (98%) and usefulness of the ML techniques for assisting Air Traffic Controllers (ATCs) to choose certain runway configuration based on real-world weather data. In the final part of this thesis, I extend the ML model for forecasting runway configuration and develop a capacity estimation model for estimating associated capacity in a medium-term horizon (6 hours). The proposed model incorporates influencing factors (i.e., wind, visibility, cloud ceiling, and operation time) as well as all possible runway layouts for predicting the most suitable configuration. As a case study for Amsterdam Schiphol Airport, one month of airport weather data and associated runway configurations are processed to train and test the developed ML model. The results, on two days of sample traffic data, demonstrate the prediction accuracy of the ML model of up to 98%. Also, it is demonstrated that the ML predicted configuration can accommodate an additional number of flights (i.e., up to 20 flights) within one hour. This shows the viability and benefits of using ML approach for maximising runway capacity

Upgrading Plan for Conventional Distribution Networks Considering Virtual Microgrid Systems

It is widely agreed that the integration of distributed generators (DGs) to power systems is an inevitable trend, which can help to solve many issues in conventional power systems, such as environmental pollution and load demand increasing. According to the study of European Liaison on Electricity grid Committed Towards long-term Research Activities (ELECTRA), in the future, the control center of power systems might transfer from transmission networks to distribution networks since most of DGs will be integrated to distribution networks. However, the infrastructure of conventional distribution networks (CDNs) has not enough capabilities to face challenges from DG integration. Therefore, it is necessary to make a long-term planning to construct smart distribution networks (SDNs). Although many planning strategies are already proposed for constructing SDNs, most of them are passive methods which are based on traditional control and operating mechanisms. In this thesis, an active planning framework for upgrading CDNs to SDNs is introduced by considering both current infrastructure of CDNs and future requirements of SDNs. Since conventional centralised control methods have limited capabilities to deal with huge amount of information and manage flexible structure of SDNs, virtual microgrids (VMs) are designed as basic units to realise decentralised control in this framework. Based on the idea of cyber-physical-socioeconomic system (CPSS), the structure and interaction of cyber system layer, physical system layer as well as socioeconomic system layer are considered in this framework to improve the performance of electrical networks. Since physical system layer is the most fundamental and important part in the active planning framework, and it affects the function of the other two layers, a two-phase strategy to construct the physical system layer is proposed. In the two-phase strategy, phase 1 is to partition CDNs and determine VM boundaries, and phase 2 is to determine DG allocation based on the partitioning results obtained in phase 1. In phase 1, a partitioning method considering structural characteristics of electrical networks rather than operating states is proposed. Considering specific characteristics of electrical networks, electrical coupling strength (ECS) is defined to describe electrical connection among buses. Based on the modularity in complex network theories, electrical modularity is defined to judge the performance of partitioning results. The effectiveness of this method is tested in three popular distribution networks. The partitioning method can detect VM boundaries and partitioning results are in accord with structural characteristics of distribution networks. Based on the partitioning results obtained in phase 1, phase 2 is to optimise DG allocation in electrical networks. A bi-level optimisation method is proposed, including an outer optimisation and an inner optimisation. The outer optimisation focus on long-term planning goals to realise autonomy of VMs while the inner optimisation focus on improving the ability of active energy management. Both genetic algorithm and probabilistic optimal power flow are applied to determine the type, size, location and number of DGs. The feasibility of this method is verified by applying it to PG&E 69-bus distribution network. The operation of SDNs with VMs is a very important topic since the integration of DGs will lead to bidirectional power flow and fault current variation in networks. Considering the similarity between microgrids and VMs, a hybrid control and protection scheme for microgrids is introduced, and its effectiveness is tested through Power Systems Computer Aided Design (PSCAD) simulation. Although more research is needed because SDNs are more complicated than microgrids, the hybrid scheme has great potential to be applied to VMs

Period Adaptation of Real-Time Control Tasks with Fixed-Priority Scheduling in Cyber-Physical Systems

Period Adaptation of Real-Time Control Tasks with Fixed Priority Schedulin

Flexible and Adaptive Real-Time Task Scheduling in Cyber-Physical Control Systems

In a Cyber-Physical Control System (CPCS), there is often a hybrid of hard real-time tasks which have stringent timing requirements and soft real-time tasks that are computationally intensive. The task scheduling of such systems is challenging and requires flexible schemes that can meet the timing requirements without being over-conservative. Fixed-priority scheduling (FPS) is a scheduling policy that has been widely used in industry. However, as an open-loop scheduler, FPS has low system dynamics and no feedback from historic operation. As the working conditions of a CPCS will change due to both internal and external factors, an improved scheduling scheme is required which can adapt to changes without a costly system redesign. In recent years, there is a large research interest in the co-design of control and scheduling systems that explicitly considers task scheduling during the design of a controller. Many of these works reveal the possibility of adapting control periods at run-time in order to accommodate varying resource requirements and to optimise CPU utilization. It is also shown that control quality can be traded off for resource usages. In this thesis, an adaptive real-time scheduling framework for CPCS is presented. The adaptive scheduler has a hierarchical structure and it is built on top of a traditional FPS scheduler. The idea of dynamic worst-case execution time is introduced and its cause and methods to identify the existence of a trend are discussed. An adaptation method that uses monitored statistical information to update control task periods is then introduced. Finally, this method is extended by proposing a dual-period model that can switch between multiple operational modes at run-time. The proposed framework can be potentially extended in many aspects and some of these are discussed in the future work. All proposals of this thesis are supported by extensive analysis and evaluations

The sustainable materials roadmap

Over the past 150 years, our ability to produce and transform engineered materials has been responsible for our current high standards of living, especially in developed economies. However, we must carefully think of the effects our addiction to creating and using materials at this fast rate will have on the future generations. The way we currently make and use materials detrimentally affects the planet Earth, creating many severe environmental problems. It affects the next generations by putting in danger the future of the economy, energy, and climate. We are at the point where something must drastically change, and it must change now. We must create more sustainable materials alternatives using natural raw materials and inspiration from nature while making sure not to deplete important resources, i.e. in competition with the food chain supply. We must use less materials, eliminate the use of toxic materials and create a circular materials economy where reuse and recycle are priorities. We must develop sustainable methods for materials recycling and encourage design for disassembly. We must look across the whole materials life cycle from raw resources till end of life and apply thorough life cycle assessments (LCAs) based on reliable and relevant data to quantify sustainability. We need to seriously start thinking of where our future materials will come from and how could we track them, given that we are confronted with resource scarcity and geographical constrains. This is particularly important for the development of new and sustainable energy technologies, key to our transition to net zero. Currently 'critical materials' are central components of sustainable energy systems because they are the best performing. A few examples include the permanent magnets based on rare earth metals (Dy, Nd, Pr) used in wind turbines, Li and Co in Li-ion batteries, Pt and Ir in fuel cells and electrolysers, Si in solar cells just to mention a few. These materials are classified as 'critical' by the European Union and Department of Energy. Except in sustainable energy, materials are also key components in packaging, construction, and textile industry along with many other industrial sectors. This roadmap authored by prominent researchers working across disciplines in the very important field of sustainable materials is intended to highlight the outstanding issues that must be addressed and provide an insight into the pathways towards solving them adopted by the sustainable materials community. In compiling this roadmap, we hope to aid the development of the wider sustainable materials research community, providing a guide for academia, industry, government, and funding agencies in this critically important and rapidly developing research space which is key to future sustainability.journal articl

Many-Core Real-Time Network-on-Chip I/O Systems for Reducing Contention and Enhancing Predictability

In safety-critical and high-integrity computing, it is important to guarantee both performance and time-predictability of I/O operations. However, with the continued growth of hardware and architectural complexity, satisfying such real-time requirements has become increasingly challenging because of complex I/O transaction paths and extensive hardware contention. This paper proposes a systematic framework with a novel I/O controller and a reconfigurable NoC, effectively optimising I/O transaction paths to encounter reduced contention. Moreover, we present a theoretical model and optimisation process to further improve real-time performance. The evaluations show that our approach outperforms state-of-the-art I/O processing techniques on a range of metrics

A novel power management and control design framework for resilient operation of microgrids

This thesis concerns the investigation of the integration of the microgrid, a form of future electric grids, with renewable energy sources, and electric vehicles. It presents an innovative modular tri-level hierarchical management and control design framework for the future grid as a radical departure from the ‘centralised’ paradigm in conventional systems, by capturing and exploiting the unique characteristics of a host of new actors in the energy arena - renewable energy sources, storage systems and electric vehicles. The formulation of the tri-level hierarchical management and control design framework involves a new perspective on the problem description of the power management of EVs within a microgrid, with the consideration of, among others, the bi-directional energy flow between storage and renewable sources. The chronological structure of the tri-level hierarchical management operation facilitates a modular power management and control framework from three levels: Microgrid Operator (MGO), Charging Station Operator (CSO), and Electric Vehicle Operator (EVO). At the top level is the MGO that handles long-term decisions of balancing the power flow between the Distributed Generators (DGs) and the electrical demand for a restructure realistic microgrid model. Optimal scheduling operation of the DGs and EVs is used within the MGO to minimise the total combined operating and emission costs of a hybrid microgrid including the unit commitment strategy. The results have convincingly revealed that discharging EVs could reduce the total cost of the microgrid operation. At the middle level is the CSO that manages medium-term decisions of centralising the operation of aggregated EVs connected to the bus-bar of the microgrid. An energy management concept of charging or discharging the power of EVs in different situations includes the impacts of frequency and voltage deviation on the system, which is developed upon the MGO model above. Comprehensive case studies show that the EVs can act as a regulator of the microgrid, and can control their participating role by discharging active or reactive power in mitigating frequency and/or voltage deviations. Finally, at the low level is the EVO that handles the short-term decisions of decentralising the functioning of an EV and essential power interfacing circuitry, as well as the generation of low-level switching functions. EVO level is a novel Power and Energy Management System (PEMS), which is further structured into three modular, hierarchical processes: Energy Management Shell (EMS), Power Management Shell (PMS), and Power Electronic Shell (PES). The shells operate chronologically with a different object and a different period term. Controlling the power electronics interfacing circuitry is an essential part of the integration of EVs into the microgrid within the EMS. A modified, multi-level, H-bridge cascade inverter without the use of a main (bulky) inductor is proposed to achieve good performance, high power density, and high efficiency. The proposed inverter can operate with multiple energy resources connected in series to create a synergized energy system. In addition, the integration of EVs into a simulated microgrid environment via a modified multi-level architecture with a novel method of Space Vector Modulation (SVM) by the PES is implemented and validated experimentally. The results from the SVM implementation demonstrate a viable alternative switching scheme for high-performance inverters in EV applications. The comprehensive simulation results from the MGO and CSO models, together with the experimental results at the EVO level, not only validate the distinctive functionality of each layer within a novel synergy to harness multiple energy resources, but also serve to provide compelling evidence for the potential of the proposed energy management and control framework in the design of future electric grids. The design framework provides an essential design to for grid modernisation