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Increasing research attention has been placed towards distributed dynamic systems that integrate sensing, computation, communication and physical processes. A prominent feature of such systems is communication among subsystems via network mediums, and distributed estimation and control schemes can enable distributed dynamic systems to carry out complicated tasks in a cooperative manner. Utilization of networks and exposure to the physical environment means that distributed systems are more vulnerable to adversaries that include attacks, faults, and disturbances. In practical distributed systems, a wide range of adversaries exist in various forms (such as deception attack and denial-of-service attack) that affect various channels (such as the sensor channel, communication channel and actuator channel). This thesis focuses on the important topic of developing resilient estimation and control schemes for distributed dynamic systems to improve the safety and performance in the presence of adversaries. Firstly, the simultaneous presence of disturbances and attacks on distributed systems is tackled by a novel three-stage estimation approach which includes anti-disturbance estimation, optimal attack detection and detection-triggered attack-resilient estimation. This approach effectively decouples the influence of multiple disturbances and false data injection attacks existing on the same channel. In some cases, heterogeneous attacks on different channels can be simultaneously injected to have a joint effect on distributed systems. Utilizing a novel event-based update scheme, an adaptive term and a distributed disturbance observer, an event-based distributed estimation approach is introduced to deal with the joint effects of aperiodic denial-of-service attacks on the communication channel and unknown deception attacks on the sensor channel. While the previous works deal with disturbances and attacks through compensation and attenuation, some attacks, namely the sparse injection attacks on sensors are potentially unbounded and cannot be dealt with the observer-based compensation approach. In such cases, it is more desirable to isolate and remove the sensor channels that are under attack. In the third section, a switching sparse attack detector based on a monitoring function utilizes the sensing redundancy to identify and remove the attacked sensor channels, and a backstepping control scheme is designed for the practical implementation of the proposed algorithm on a robotic manipulator. In the final section, adversaries on the actuator channel are studied, where the challenging topic of unknown direction faults on multi-input-multi-output distributed systems is dealt with novel Nussbaum functions, and a distributed containment control scheme is proposed for a network of uncertain nonlinear agents. Moreover, an event-triggering mechanism is introduced to avoid continuous communication among agents. The main contribution of this thesis is presenting a framework for the resilient estimation and control of distributed network systems against a wide range of adversaries, including but not limited to injection attacks, denial of service attacks, actuator faults and disturbances. The approaches introduced in each chapter of this thesis have compelling features that can be either implemented on their own, or integrated with other existing control and estimation schemes to enhance the resilience of distributed systems.

For keeping abreast with the steps of wireless communication system development, especially for 5G, antennas that can transmit signals at higher rate and are compatible for different frequency ranges, especially MIMO (multiple-input multiple-output), array and multi-band antennas, have been continuously researched with high expectations. These new generation antennas should be able to efficiently improve data rate, transmission speed and channel capacity, as well as fit in the IoE mass applications. To maximize the integration viability of 5G wireless architectures, the flexibility and conformity of next-generation electronics as well as the environmental amity of their materials and related fabrications are the essential features to achieve a more robust and reliable revolutionized end-user experience. Among all the flexible conductive materials, graphene has surpassed others for its excellent flexibility, conductivity, low cost and strength, been proved a strong candidate for next generation indispensable soft electronics in wireless communication and sensing applications. However, the research of graphene is still at the beginning state. A number of issues still need to be addressed, such as: complicated structures of different kinds of antennas need to be specially designed to enhance the gain and cope with the loss introduced by graphene material comparing to normal metal; a more sustainable method should be investigated bypassing toxic solvents, complex fabrication, expensive materials, and high temperature annealing; the conductivity and stability of the ink should be improved; large-scale and affordable fabrication process is also in urgent need, where lies the novelty of the work: an aggregation of different antenna technologies and graphene printing electronics, enabling cheap accessible and green antennas with high performance to be massively integrated in IoT applications. In this thesis, different designs of antennas, i.e., MIMO, array, dual- and tri- bands, RFID tag antennas, with excellent radiation performance and flexibility have been integrated with low-cost, biodegradable printed graphene technique to conquer the most concerned surge in electronic waste caused by the mass production of antennas spurred by the significant deployment of IoE applications. These specially designed sustainable antennas provide strong support of 5G wireless communication mass data throughput and high performance IoT applications, guaranteeing high-speed data transmission and system reliability, and can be easily applied in flexible 5G front ends, various IoT systems, as well as in next-generation wearable electronic devices. In addition, this dissertation has successfully investigated and explored a low-cost sustainable way for fabricating screen-printing graphene inks with high conductivity, presenting the novel aggregation of high-quality graphene printing technology and large-scale industrial production of flexible electronic devices in microwave region.

This thesis mainly focuses on the motion control problems of the wearable lower limb exoskeleton robot. Firstly, by approximating the ideal human gait as a cyclic signal, the motion control problem of the exoskeleton robot is converted to a tracking problem in a series of fixed and finite time intervals, where each interval represents a gait cycle. Then, a novel iterative learning control algorithm has been proposed. The proposed algorithm combines the concept of feedback linearization with the iterative learning control method and has advantages in response speed, tracking accuracy, and error convergence rate. Relative simulation results are given to demonstrate the effectiveness of the proposed algorithm. Finally, the adaptive oscillator method which is also feasible for the problem has been introduced and a comparison between the two methods is given. Both advantages and disadvantages of the proposed iterative learning control algorithm are discussed.

The rapid development of wireless communication, space-based radar systems and active military radars in the past decades gives rise to the necessary of information security and electromagnetic waves pollution, which arouse the requirement of perfect electromagnetic absorbers in both military and civil areas. Since the isolation of graphene and the first experimental demonstration of so-called perfect metamaterial absorber carried out in the early 21st century, a large amount of research has been directed at graphene based metamaterial absorbers. This thesis delivers pioneering developments on the design of printed graphene metamaterial absorbers on electromagnetic ranges including radio frequency, microwave frequency, and THz bands. This thesis focuses on the design and optimization of broadband metamaterial absorbers for civil and military applications and aims to provide novel ideas and combed design approaches for further development of the metamaterial absorbers with outstanding properties. This thesis presents several broadband printed graphene metamaterial absorbers with competitive properties. For example, the two multilayer structure metamaterial absorbers in chapter 3 provide ultra-wide operation bandwidth covering from C band to Ka band, and from S band to Ku band, respectively; the ultra-thin metamaterial absorber in chapter 4 achieves a relative absorption bandwidth of 178% with a thickness of merely 0.3 mm; the novel printed graphene AMC based radar absorber has pioneering advances compared with conventional metallic AMC based absorbers; the genetic algorithm optimized absorber indicates potentials of coding metamaterial absorbers. For the first time, the printed graphene AMC based radar absorber is experimentally demonstrated. Furthermore, the printed graphene metamaterial absorbers presented in this thesis are angle-insensitive and polarization-independent, and possess good flexibility thanks to the printed graphene patterns and flexible dielectric substrates. This work significantly provides novel approaches and more design flexibility to metamaterial absorbers, and expands the applications of printed graphene in the design and fabrication of metamaterial absorbers.

Smart textile or electro-textile (e-textile) is defined as fabrics constructed entirely or partially by electrically conductive material, which can be integrated with clothing as a part of bodyworn electronics. The main features of e-textile that make it so desirable are its flexibility, durability and high corrosion resistance as a type of daily carry-on electronic device. Over the past decade, e-textile antennas have gained tremendous attention. The close-body nature of such antennas possesses great potential in various fields such as daily communication, personal healthcare and body area networks (BANs). However, due to flexible structures of the e-textile, it can be easily affected by body movements. Therefore, the challenges exist in the application of wearable antennas are the factors that are difficult to predict and control. The purpose of this thesis is to present comprehensive study into multiple types of e-textile integrated antennas and their real-life applications, including NFC antennas integrated body sensing device, RFID antennas and broadband monopole antennas integrated body-worn energy harvesting systems. This thesis is comprised of three main research areas. The first area of this work focuses on the development of novel e-textile embroidered wearable near-field communication RFID antennas, which includes antenna design through full electromagnetic wave simulation, fabrication with conductive treads and textile substrates and measurements under various circumstances such as mechanical bending and human body effects. The measurement results show that the proposed NFC antenna is able to operate under significant degree of bending as well as direct skin contact attributed to its broad operating bandwidth. Distinguished from conventional NFC antennas, e-textile NFC antennas are ensured to be capable of communicating at desired operating frequency of 13.56 MHz while placed on almost any place on clothes. Based on this design, a smart textile integrated wireless powered near field communication (NFC) body temperature and sweat sensing system has also been developed. In this research, temperature and sweat sensors are embedded and powered by an e-textile NFC antenna enabling battery-free and wireless sensing operation. The proposed device is seamlessly integrated with close-fitting garments and the sensor data can be acquired with NFC readers and smart phones, which achieves realtime health monitoring in a convenient and non-intrusive way. The sensor data can be accessed with maximum read distance of 6 cm. The on-body accuracy of the temperature and sweat sensors is measured as ±0.14°C and ±0.2%, respectively. The proposed device aims to 13 provide ubiquitous wireless and battery-free connectivity for daily healthcare and wellbeing monitoring. The second area includes design, fabrication and measurements of e-textile integrated antennas for various applications. Two designs are presented in this part. The first one is a machine embroidered wearable e-textile wideband UHF RFID tag antenna. The antenna read distance is above 6.5 m between 860 MHz and 960 MHz. The tag reading has been tested both in air and on body where the maximum read distance are 7.23 m and 4.71 m, respectively. The second design is a novel e-textile integrated wideband monopole antenna that aims to be applied in body-worn RF energy harvesting systems. The proposed antenna is constructed with silver coated conductive threads woven into cotton substrate. A novel dual circular patch structure is introduced to achieve a wide operating bandwidth which covers GSM bands (range 870.4-915 MHz and 1.7-1.9 GHz), LTE bands (range 0.79-0.96 GHz; 1.71-2.17 GHz; and 2.5-2.69 GHz) and Wi-Fi frequencies (2.4 and 3.6 GHz). The last area under investigation is to develop a functional on-body ambient RF energy harvesting system by combining an e-textile antenna array with a multiband low-input rectifier. The antenna array is constructed with two identical broadband monopole antennas, which achieves relatively high gain and efficiency at most occupied ambient RF frequency bands. A compact six-band rectifier is integrated with the textile antenna with corresponding working frequencies. Experimental results show that the harvesting device performs stably with low input power down to -20dBm, and it is able to generate highest DC power of -11.67 dBm with a wireless power source at a single frequency (0.9 GHz). This work has successfully demonstrated design and performance of a number of novel etextile fabricated antennas and antenna-based electronics (wireless sensors and RF energy harvester). The challenges over this topic such as human body effects and external forces that might be taken by the antennas have been discussed and overcome. This work is believed to be a great contribution to the field of wearable electronics.

This study considers the distributed affine formation control problem of networked multi-agent systems. In affine formation manoeuvre control, the agents are to be capable of producing specified geometric patterns and simultaneously accomplish required manoeuvres, such as scales, translations and rotations. Here, the formation control problem is studied using the stress matrix approach which has similar properties as the Laplacian matrix of a graph. The major difference is that the edge weights can have positive or negative values and can be considered as the generalized Laplacian matrix of a graph. In this study, we commence by considering the scenario where the dynamics of the agents are defined using triple-integrator dynamics. This is inspired by the consideration that a broad range of systems can be modelled by triple-integrator dynamics. For instance, the DC motor which serves as actuator in most mechanical control systems. The longitudinal dynamics of individual vehicles in an n-vehicle system travelling on a single lane in a drive-train model is approximated by triple-integrator dynamics in some existing literature. It is therefore important to widen the application area by considering triple-integrator agent dynamics. Here, the cases where the inter-agent communications are in continuous-time and sampled-data are considered. Under the proposed control laws, the group of agents are able to track time-varying targets that are affine transformations of a given nominal formation, and the desired formation maneuvers are only known by the leaders. Furthermore, the affine formation control problem of general linear systems with uncertainty is considered. A variety of control laws are presented to address different cases. The proposed laws consider the general linear case, the case with uncertainty and the fully distributed case using robust and adaptive strategies. Under the proposed laws, the collection of agents can track any targets that are affine transforms of a defined reference configuration. Experimental results are presented to demonstrate the effectiveness of the proposed control laws.

Multi-agent systems have received a significant amount of research attention during the last three decades. As one of the most important problems, distributed optimisation for multi-agent systems has been actively studied in recent years, owing to its wide applications in engineering, social and economic networks. In this thesis, two different types of optimisation problems are considered: the cooperative and competitive optimisation. In network cooperation, the agents aim to optimise the summation of all local objectives. A distributed algorithm is proposed to deal with the multi-objective resource allocation problem with affine constraints. The framework encompasses a group of distributed decision makers in the subagents, where each of them possesses a local preference index. Novel distributed algorithms are proposed to solve such a problem in a distributed manner. The weighted L_p preference index is utilised in each agent since it can provide a robust Pareto solution to the problem. By using distributed fixed-time optimisation methods, the L_p preference index is constructed online without specifying the unknown parameters. Furthermore, consensus-based fixed-time algorithms are deployed to solve time-varying optimisation problems. Since the cost functions and their derivatives are usually impossible to be expressed by explicit functions due to the complexity of modern systems, function calls have to be performed to obtain those values. Therefore, a surrogate-assisted method is proposed to approximate the true functions and to optimise the black-box functions. A balanced infill strategy for exploration and exploitation is designed to optimise the current model and to improve the surrogate fitting. Distributed algorithms are further employed to solve this surrogate-based optimisation problem, and convergence of those algorithms are analysed. Seeking a Nash equilibrium is the ultimate goal for distributed competitive games. The agents are not assumed to have direct access of other agents' states, and instead, they estimate other agents' states by communicating with their neighbours. Advanced consensus algorithms are implemented for such purposes, and consequently the game is decentralised into a set of subsystems interacting over a communication network. Affine constraints are introduced in the problem settings such that local decision vectors are coupled. Fixed-gain design and adaptive design are developed to search the generalised Nash equilibrium. Moreover, to speed up the convergence, fixed-time algorithms are designed to obtain the Nash equilibrium within a bounded time.

The research in this thesis mainly focuses on the power system management and optimisation by distributed methods. The traditional power system management and optimisation approaches are based on centralised fashions. However, it is difficult or impossible to establish a single centralised control centre in modern distributed power systems. As the amount of data in the power system continues to increase, centralised algorithms are limited by insufficient computing power. To solve the problems mentioned above, we will look into the distributed optimisation problem in power system. Firstly, to deal with the initialisation error and local physical constraints, a distributed demand side management strategy is studied by maximising the total welfare from demand side to supply side. To achieve the objectives of demand side management, controllable power units generate their optimal power reference based on the proposed distributed algorithm by coordinating information with the neighbours. Thus, it is a completely distributed algorithm, and the analysis denotes that the proposed algorithm can solve the optimal economic dispatch problem in an initialisation-free approach and adapt to the plug-and-play operation. Secondly, a uncertain power market environment is considered in this thesis. To maximise the benefit of a battery energy storage system in power markets, a novel reinforcement learning based optimal bidding strategy is investigated. The objective is to extend the battery life and maximise the benefit for battery energy storage system owners. Using this strategy, the battery energy storage system can make different decisions based on various environmental states. Meanwhile, the proposed bidding strategy overcomes the discrete limits by the function approximation approach. Finally, from the above researches, it is noticed that the data volume and customer privacy may influence the reliability of the power systems. To avoid the computing burden and consider the data privacy. A novel Markovian switching based distributed deep belief networks for short term load forecasting is introduced. The proposed algorithm trains the short-term load forecasting model with local datasets and update the model parameters by communicating with connected neighbours, which does not need to transfer any information about the load data. In addition, it can simultaneously achieve fast training speed and superior robustness to cyberattacks due to the Markovian switching structure and the unsupervised training process of the deep belief networks.

The Internet of Things (IoT) extends the electronic connectivity into millions of IoT nodes around the world. In order to achieve the autonomy to make sound decisions based on the collected and analysed information, artificial intelligence algorithms are for networks to leverage to turn into autonomous IoT (AIoT). The core value of these AIoT applications is from analysing the information provided by these smart entities and performing autonomous decision-making. However, general issues in networks, such as the outlier raging, communication overhead, memory shortage, etc., render the information fusion process ineffective, which leads to the unreliable AIoT applications. Thus, the advanced information fusion methods must be proposed to address such issues for the sake of fully utilizing these smart applications and services. To solve such real-life problems, various information fusion approaches are developed for the AIoT systems. For the outlier raging problems in AIoT applications, the distance-based method inspired by the KNN rule has been used. In addition, artificial intelligence algorithms, such as the deep learning and reinforcement learning, are integrated with consensus theories to handle the distributed learning problems in AIoT applications for the sake of solving the communication overhead and memory shortage in networks. Also, the comprehensive evaluation works show the proposed approaches emerge the powerful capability, and they provide important operational advantages over traditional centralized methods and typical distributed approaches. The main contributions of this thesis are proposing two outlier detection methods with compelling features, such as low computational complexity and memory usage, for the outlier raging in networks, and one novel distributed learning framework that can save the average training time and memory storage compared with the typical methods.

This thesis addresses problems arising in the coordination and estimation of a connected multi-agent system. The distributed coordination of agents consists of cooperative control and optimisation. Cooperative control aims at guiding a team of agents moving in a formation, maintaining a required pattern or velocity. An optimisation problem in a group of agents aims at driving them towards specific locations according to a performance index. Furthermore, the distributed estimation problem when states in a dynamical process are unknown is also considered; the agents are then assigned to estimate the unknown states cooperatively. A coordination problem investigated in this thesis is the coverage control problem in robot sensor networks whose objective is to find the optimal locations of the sensors leading to the best measurement. In this work, the objective function of locational optimisation is solved by interchanging the positions of the neighbouring agents. The formulated Lagrangian function augments the objective function and the consensus constraint to accommodate this mechanism. Accordingly, a coverage controller with cooperative constraint is proposed via the gradient-descent protocol. Furthermore, a controller is designed to drive the robots to the optimal locations in a finite time regardless of their initial positions. However, since an environment is not generally convex due to the presence of obstacles, a coverage controller with potential-field based obstacle avoidance is also considered to drive the robot to the optimal location safely. Motivated by the unavailability of information distribution in an environment before sensor deployment, a class of distributed nonlinear filter is designed to estimate the states of a dynamical process using the shared information among the agents. The proposed algorithm extends the distributed unscented Kalman filter to accommodate any communication topology. It utilises not only the measurement from an agent's sensor for estimating the process but also the shared information. The proposed filter is then employed to estimate the information distribution in the optimal coverage problem with unknown information distribution. Different from the existing field-estimation algorithms, the proposed filter also optimises the estimator gains in every iteration to avoid instability of the system caused by failure to choose the appropriate parameters. Thereafter, the question regarding how good is a distributed recursive Bayesian-based estimator when the information belongs to more general manifolds other than Euclidean, i.e., Riemannian manifolds, are also addressed. In this thesis, the formulated intrinsic Cramér-Rao bounds (ICRBs) demonstrates that the non-zero curvature term of the manifold also affects the performance of the Bayesian estimator. Lastly, assuming that the probability density functions (PDF) of the noises are Gaussian, a distributed nonlinear Kalman filter for Riemannian systems is also proposed. The simulations verify that the mean-squared error of the designed filter will not be lower than its ICRBs.

As graphene has been discovered since 2004, a large amount of papers investigating the fundamental physical and chemical properties of graphene has been published. However, the applications that perfect taking advantage of graphene's properties are bare. This thesis focused on the development of graphene and graphene oxide (GO) based materials and their applications for electromagnetic ranges such as antennas, absorbers, wireless energy harvesting devices, sensors, and so on. In this thesis, physical exfoliation of graphene from graphite particles has been well discussed and compared. Both popular bath sonication and mechanical shear-mixing methods have been used and compared. The cost, concentration and conductivity of exfoliated graphene have been optimized. Moreover, the graphene based low-cost screen printing inks have been developed and discussed in this thesis. Excellent conductivity property allows engineers to produce wireless connectivity antenna operational from MHz to tens of GHz. Applications of printed, flexible Near Field Communication antenna (NFC), ultra high frequency Radio Frequency Identification (RFID) tags, wireless NFC sensors and ultra-wideband energy harvesting system have been designed, fabricated and demonstrated in this thesis. Due to the advances of ink preparation, cost and electrical properties, chemically exfoliated GO is used to replace physical exfoliated graphene on certain occasions. Printing and novel reduction process which can control reduced graphene oxide's (rGO) conductivity has been demonstrated as well. The fabricated rGO laminate not only can be used in flexible electronic circuits as conductors or resistors but also highly efficient to absorb microwave radiations with proper design. A printed Jaumann structured microwave absorber has been characterized and compared with standard absorber which is consisted by graphene loaded composites. This thesis also presents the latest microfluidic RFID sensors for stretch sensing. The liquid, high conductive alloy based conductor has excellent sensitivity and repeatability comparing with lossy, rigid graphene based materials.

Four trials were conducted to investigate the potentials of applying microwave technology in bio-sensing, material characterization, and in the measurement of glucose concentration in standard sugar solutions, biological tissue phantoms, and human blood. The objective was to design and fabricate a Microstrip Resonator capable of measuring blood glucose levels non-invasively through human finger for quick detection of diabetes condition and its healthcare management. In all, four different Microstrip Resonators (i.e., 1, 2, 3, and 4) were designed using CST Studio, fabricated, and tested on standard glucose solutions of varying concentrations covering the range existing in normal to diabetes conditions, blood tissue phantoms mimicking human blood, and human subjects' fingers for blood glucose measurement, non-invasively. The S-parameters extracted from the results of reflection and transmission coefficients of the test materials after exposure to and interaction with the microwave were converted to permittivity values. For the standard glucose solutions, the permittivity values were plotted against glucose concentration to obtain a glucose standard curve, the equation of which was used to estimate glucose levels in the blood of the human subjects' fingers. Established an invasive method of glucose measurement using Glucometer was employed simultaneously as control. Percent deviations of the glucose concentration values obtained from the Microstrip Resonator technique from the corresponding glucose concentration reading on the Glucometer were calculated to ascertain the accuracy of the determination of the former method. Also, the mean values of blood glucose of human subjects' fingers from both ways were compared for the significant difference using the Student-T test at α 0.05. An added advantage of the non-invasive technique of Microstrip Resonator is the simplicity, non-associated with pain, and the ease of use on-the-go.

The information exchanged among agents over a discrete-time network due to quantization reduce the performance of the multi-agent systems. In this thesis, the performance effect of this restriction is examined. Subsequently, the necessary and sufficient condition to guarantee consensus of the quantized nonlinear multi-agent systems is provided using the Kalman-Yakubovich-Popov (KYP) lemma. However, the KYP lemma depends on the existence of some real functions, the passivity of the multi-agent systems can be determined using the KYP lemma and the quantized information. Necessary and sufficient condition is given to guarantee the consensus of the multi-agent systems. An observer-based quantized consensus control for Lipschitz nonlinear systems with Lyapunov-Krasovskii function is designed. The observer is expressed in linear matrix inequalities (LMIs) to ensured the passivity of the discrete-time system. The quantized consensus control and asymptotic stability of the system are solved using the Lyapunov functions. The quantized information is used to achieve a consensus of the proposed multi-agent system. Also, the quantized consensus control of a discrete-time Lipschitz nonlinear multi-agent systems with input delay is investigated. In other to deal with the input delay, the Artstein's reduction model approach is used, alongside a set of linear matrix inequalities and the quantized information for the analysis. Hence, the quantized consensus control and stability of the proposed control systems can be achieved in the time domain using the Lyapunov approach. Furthermore, the quantized consensus control of Lipschitz nonlinear discrete-time multi-agent systems with truncated prediction approach and input delay is addressed to determine the response of each agent. The input delay is solved using the truncated predicted method, with this method and the Lyapunov-Krasovskii functional, alongside the given assumption, the quantized consensus protocol is proposed. The proposed method is employed to guarantee the consensus of the quantized nonlinear Lipschitz systems. Finally, simulation examples are presented to illustrate the effectiveness of the results.