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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.