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

Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information

This paper addresses the problem of resilient consensus in the presence of misbehaving nodes. Although it is typical to assume knowledge of at least some nonlocal information when studying secure and fault-tolerant consensus algorithms, this assumption is not suitable for large-scale dynamic networks. To remedy this, we emphasize the use of local strategies to deal with resilience to security breaches. We study a consensus protocol that uses only local information and we consider worst-case security breaches, where the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach consensus despite the influence of the malicious nodes under different threat assumptions. These conditions are stated in terms of a novel graph-theoretic property referred to as network robustness.Comment: This report contains the proofs of the results presented at HiCoNS 201

Defense and Tolerance Technique Against Attacks and Faults on Leader-Following Multi-USVs

This study explores the leader-following consensus tracking control issue of multiple unmanned surface vehicles (multi-USVs) in the presence of malicious connectivity-mixed attacks in the cyber layer, and concurrent output channel noises, sensor/actuator faults, and wave-induced disturbances in the physical layer. Sensor/actuator faults are initially modeled with unified incipient and abrupt features. Additionally, connectivity-mixed attacks are depicted using connectivity-paralyzed and connectivity-maintained topologies through nonoverlapping and switching iterations. The standardization and observer design in multi-USVs are incorporated to decouple the augmented dynamics and estimate unknown state, fault, and noise observations, and then a defense and fault-tolerant consensus tracking control approach is designed to accomplish the robustness to disturbances/noises, resilience to attacks, and tolerance to faults, simultaneously. The criteria for achieving leader-following exponential consensus tracking of multi-USVs with cyber-physical threats can be determined based on activation rate and attack frequency indicators. Comparative simulations outline the effectiveness and economy of the proposed defense and tolerance technique against sensor/actuator faults and cyber-attacks on multi-USVs

Distributed Fault-Tolerant Control for Networked Robots in the Presence of Recoverable/Unrecoverable Faults and Reactive Behaviors

The paper presents an architecture for distributed control of multi-robot systems with an integrated fault detection, isolation, and recovery strategy. The proposed solution is based on a distributed observer-controller schema where each robot, by communicating only with its direct neighbors, is able to estimate the overall state of the system; such an estimate is then used by the controllers of each robot to achieve global missions as, for example, centroid and formation tracking. The information exchanged among the observers is also used to compute residual vectors that allow each robot to detect failures on anyone of the teammates, even if not in direct communication. The proposed strategy considers both recoverable and unrecoverable actuator faults as well as it properly manages the possible activation of reactive local control behaviors of the robots (e.g., the activation of obstacle avoidance strategy), which generate control inputs different from those required by the global mission control. In particular, when the robots are subject to recoverable faults, those are managed at a local level by computing a proper compensating control action. On the other side, when the robots are subject to unrecoverable faults, the faults are isolated from anyone of the teammates by means of a distributed fault detection and isolation strategy; then, the faulty robots are removed from the team and the mission is rearranged. The proposed strategy is validated via numerical simulations where the system properly identifies and manages the different cases of recoverable and unrecoverable actuator faults, as well as it manages the activation of local reactive control in an integrated case study

Finite-Time Resilient Formation Control with Bounded Inputs

In this paper we consider the problem of a multi-agent system achieving a formation in the presence of misbehaving or adversarial agents. We introduce a novel continuous time resilient controller to guarantee that normally behaving agents can converge to a formation with respect to a set of leaders. The controller employs a norm-based filtering mechanism, and unlike most prior algorithms, also incorporates input bounds. In addition, the controller is shown to guarantee convergence in finite time. A sufficient condition for the controller to guarantee convergence is shown to be a graph theoretical structure which we denote as Resilient Directed Acyclic Graph (RDAG). Further, we employ our filtering mechanism on a discrete time system which is shown to have exponential convergence. Our results are demonstrated through simulations

Guaranteed Performance Consensus for Multi-Agent Systems

The guaranteed performance of the consensus control for multi-agent systems with Lipschitz nonlinear dynamics and directed interaction topologies is investigated, where the directed interaction topology contains a spanning tree. By a special matrix transformation, guaranteed performance consensus problems are transferred into guaranteed performance stabilization problems. Then, the criterions of guaranteed performance consensus for nonlinear multi-agent systems with directed interaction topologies are obtained, and an upper bound of the introduced performance function is given. A numerical simulation is given to demonstrate the effectiveness of the proposed results. Finally, some possible topics about the guaranteed performance consensus problem for nonlinear multi-agent systems are proposed