Data-Driven Architecture to Increase Resilience In Multi-Agent Coordinated Missions

The rise in the use of Multi-Agent Systems (MASs) in unpredictable and changing environments has created the need for intelligent algorithms to increase their autonomy, safety and performance in the event of disturbances and threats. MASs are attractive for their flexibility, which also makes them prone to threats that may result from hardware failures (actuators, sensors, onboard computer, power source) and operational abnormal conditions (weather, GPS denied location, cyber-attacks). This dissertation presents research on a bio-inspired approach for resilience augmentation in MASs in the presence of disturbances and threats such as communication link and stealthy zero-dynamics attacks. An adaptive bio-inspired architecture is developed for distributed consensus algorithms to increase fault-tolerance in a network of multiple high-order nonlinear systems under directed fixed topologies. In similarity with the natural organisms’ ability to recognize and remember specific pathogens to generate its immunity, the immunity-based architecture consists of a Distributed Model-Reference Adaptive Control (DMRAC) with an Artificial Immune System (AIS) adaptation law integrated within a consensus protocol. Feedback linearization is used to modify the high-order nonlinear model into four decoupled linear subsystems. A stability proof of the adaptation law is conducted using Lyapunov methods and Jordan decomposition. The DMRAC is proven to be stable in the presence of external time-varying bounded disturbances and the tracking error trajectories are shown to be bounded. The effectiveness of the proposed architecture is examined through numerical simulations. The proposed controller successfully ensures that consensus is achieved among all agents while the adaptive law v simultaneously rejects the disturbances in the agent and its neighbors. The architecture also includes a health management system to detect faulty agents within the global network. Further numerical simulations successfully test and show that the Global Health Monitoring (GHM) does effectively detect faults within the network

Resilience Model for Teams of Autonomous Unmanned Aerial Vehicles (UAV) Executing Surveillance Missions

Teams of low-cost Unmanned Aerial Vehicles (UAVs) have gained acceptance as an alternative for cooperatively searching and surveilling terrains. These UAVs are assembled with low-reliability components, so unit failures are possible. Losing UAVs to failures decreases the team\u27s coverage efficiency and impacts communication, given that UAVs are also communication nodes. Such is the case of a Flying Ad Hoc Network (FANET), where the failure of a communication node may isolate segments of the network covering several nodes. The main goal of this study is to develop a resilience model that would allow us to analyze the effects of individual UAV failures on the team\u27s performance to improve the team\u27s resilience. The proposed solution models and simulates the UAV team using Agent-Based Modeling and Simulation. UAVs are modeled as autonomous agents, and the searched terrain as a two-dimensional M x N grid. Communication between agents permits having the exact data on the transit and occupation of all cells in real time. Such communication allows the UAV agents to estimate the best alternatives to move within the grid and know the exact number of all agents\u27 visits to the cells. Each UAV is simulated as a hobbyist, fixed-wing airplane equipped with a generic set of actuators and a generic controller. Individual UAV failures are simulated following reliability Fault Trees. Each affected UAV is disabled and eliminated from the pool of active units. After each unit failure, the system generates a new topology. It produces a set of minimum-distance trees for each node (UAV) in the grid. The new trees will thus depict the rearrangement links as required after a node failure or if changes occur in the topology due to node movement. The model should generate parameters such as the number and location of compromised nodes, performance before and after the failure, and the estimated time of restitution needed to model the team\u27s resilience. The study addresses three research goals: identifying appropriate tools for modeling UAV scenarios, developing a model for assessing UAVs team resilience that overcomes previous studies\u27 limitations, and testing the model through multiple simulations. The study fills a gap in the literature as previous studies focus on system communication disruptions (i.e., node failures) without considering UAV unit reliability. This consideration becomes critical as using small, low-cost units prone to failure becomes widespread

Fault Detection and Isolation in Controlled Multi-Robot Systems

Multi-Agent Systems (MASs) have attracted much popularity, since the previous decade due to their potential wide range of applications. Indeed, connected MASs are deployed in order to achieve more complex objectives that could otherwise not be achievable by a single agent. In distributed schemes, agents must share their information with their neighbours, which are then used for common control and fault detection purposes, and thus do not require any central monitoring unit. This translates into the necessity to develop efficient distributed algorithms in terms of robustness and safety. Indeed, the problem of safety in connected cooperative MASs has arisen as a consequence of their complexity and the nature of their operations and wireless communication exchanges, which renders them vulnerable to not only physical faults, but also to cyber-attacks. The main focus of this thesis is the study of distributed fault and attack detection and isolation in connected MASs. First, a distributed methodology for global detection of actuator faults in a class of linear MASs with unknown disturbances is proposed using a cascade of fixed-time Sliding Mode Observers (SMOs), where each agent having access to their state, and neighbouring information exchanges, can give an exact estimate of the state of the overall MAS. An LMI-based approach is then applied to design distributed global robust residual signals at each agent capable of detecting faults anywhere in the network. This is then extended to agents with nonlinear nonholonomic dynamics where a new distributed robust Fault Detection and Isolation (FDI) scheme is proposed using predefined-time stability techniques to derive adequate distributed SMOs. This enables to reconstruct the global system state in a predefined-time and generate proper residual signals. The case of MASs with higher order integrator dynamics, where only the first state variable is measurable and the topology is switching is investigated, where a new approach to identify faults and deception attacks is introduced. The proposed protocol makes an agent act as a central node monitoring the whole system activities in a distributed fashion whereby a bank of distributed predefined-time SMOs for global state estimation are designed, which are then used to generate residual signals capable of identifying cyber-attacks despite the switching topology. The problem of attack and FDI in connected heterogeneous MASs with directed graphs, is then studied. First, the problem of distributed fault detection for a team of heterogeneous MASs with linear dynamics is investigated, where a new output observer scheme is proposed which is effective for both directed and undirected topologies. The main advantage of this approach is that the design, being dependant only on the input-output relations, renders the computational cost, information exchange and scalability very effective compared to other FDI approaches that employ the whole state estimation of the agents and their neighbours as a basis for their design. A more general model is then studied, where actuator, sensor and communication faults/attacks are considered in the robust detection and isolation process for nonlinear heterogeneous MASs with measurement noise, dynamic disturbances and communication parameter uncertainties, where the topology is not required to be undirected. This is done using a distributed finite-frequency mixed H_/H1 nonlinear UIO-based approach. Simulation examples are given for each of the proposed algorithms to show their effectiveness and robustness

Event-triggered Consensus Frameworks for Multi-agent Systems

Recently, distributed multi-agent systems (MAS) have been widely studied for a variety of engineering applications, including cooperative vehicular systems, sensor networks, and electrical power grids. To solve the allocated tasks in MASs, each agent autonomously determines the appropriate actions using information available locally and received from its neighbours. Many cooperative behaviours in MAS are based on a consensus algorithm. Consensus, by definition, is to distributively agree on a parameter of interest between the agents. Depending on the application, consensus has different configurations such as leader-following, formation, synchronization in robotic arms, and state estimation in sensor networks. Consensus in MASs requires local measurements and information exchanges between the neighbouring agents. Due to the energy restriction, hardware limitation, and bandwidth constraint, strategies that reduce the amount of measurements and information exchanges between the agents are of paramount interest. Event-triggering transmission schemes are among the most recent strategies that efficiently reduce the number of transmissions. This dissertation proposes a number of event-triggered consensus (ETC) implementations which are applicable to MASs. Different performance objectives and physical constraints, such as a desired convergence rate, robustness to uncertainty in control realization, information quantization, sampled-data processing, and resilience to denial of service (DoS) attacks are included in realization of the proposed algorithms. A novel convex optimization is proposed which simultaneously designs the control and event-triggering parameters in a unified framework. The optimization governs the trade-off between the consensus convergence rate and intensity of transmissions. This co-design optimization is extended to an advanced class of event-triggered schemes, known as the dynamic event-triggering (DET), which is able to substantially reduce the amount of transmissions. In the presence of DoS attacks, the co-design optimization simultaneously computes the control and DET parameters so that the number of transmissions is reduced and a desired level of resilience to DoS is guaranteed. In addition to consensus, a formation-containment implementation is proposed, where the amount of transmissions are reduced using the DET schemes. The performance of the proposed implementations are evaluated through simulation over several MASs. The experimental results demonstrate the effectiveness of the proposed implementations and verify their design flexibility

A graph automorphic approach for placement and sizing of charging stations in EV network considering traffic

This paper proposes a novel graph-based approach with automorphic grouping for the modelling, synthesis, and analysis of electric vehicle (EV) networks with charging stations (CSs) that considers the impacts of traffic. The EV charge demands are modeled by a graph where nodes are positioned at potential locations for CSs, and edges represent traffic flow between the nodes. A synchronization protocol is assumed for the network where the system states correspond to the waiting time at each node. These models are then utilized for the placement and sizing of CSs in order to limit vehicle waiting times at all stations below a desirable threshold level. The main idea is to reformulate the CS placement and sizing problems in a control framework. Moreover, a strategy for the deployment of portable charging stations (PCSs) in selected areas is introduced to further improve the quality of solutions by reducing the overshooting of waiting times during peak traffic hours. Further, the inherent symmetry of the graph, described by graph automorphisms, are leveraged to investigate the number and positions of CSs. Detailed simulations are performed for the EV network of Perth Metropolitan in Western Australia to verify the effectiveness of the proposed approach

Security of Cyber-Physical Systems

Cyber-physical system (CPS) innovations, in conjunction with their sibling computational and technological advancements, have positively impacted our society, leading to the establishment of new horizons of service excellence in a variety of applicational fields. With the rapid increase in the application of CPSs in safety-critical infrastructures, their safety and security are the top priorities of next-generation designs. The extent of potential consequences of CPS insecurity is large enough to ensure that CPS security is one of the core elements of the CPS research agenda. Faults, failures, and cyber-physical attacks lead to variations in the dynamics of CPSs and cause the instability and malfunction of normal operations. This reprint discusses the existing vulnerabilities and focuses on detection, prevention, and compensation techniques to improve the security of safety-critical systems

Large-Scale Distributed Coalition Formation

The CyberCraft project is an effort to construct a large scale Distributed Multi-Agent System (DMAS) to provide autonomous Cyberspace defense and mission assurance for the DoD. It employs a small but flexible agent structure that is dynamically reconfigurable to accommodate new tasks and policies. This document describes research into developing protocols and algorithms to ensure continued mission execution in a system of one million or more agents, focusing on protocols for coalition formation and Command and Control. It begins by building large-scale routing algorithms for a Hierarchical Peer to Peer structured overlay network, called Resource-Clustered Chord (RC-Chord). RC-Chord introduces the ability to efficiently locate agents by resources that agents possess. Combined with a task model defined for CyberCraft, this technology feeds into an algorithm that constructs task coalitions in a large-scale DMAS. Experiments reveal the flexibility and effectiveness of these concepts for achieving maximum work throughput in a simulated CyberCraft environment