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

Agent based modeling of power distribution systems

The electric power system is a very vast network and becoming more complex each day. The traditional vertically monopolistic structure has been deregulated and replaced by gencos, transcos and, discos; increasing the power system intricacy. During the past few decades there has been remarkable development in software and hardware technologies for the analysis and design activities in power system planning, operation, and control. However, much still depends on the judgment of human experts. A single fault in power system can lead to multiple faults and can collapse the whole system. Power System needs a more decentralized control mechanism for solving these problems. One novel solution would be Multi-agent Systems. A Multi-agent system is a collection of agents, which perceives the system changes and acts on the system in order to achieve its goals. Recent technology developments in the area of Multi-agent systems making it a viable solution for today\u27s complicated power network.;A Multi-agent system model is developed for fault detection and reconfiguration in this thesis work. These models are developed based on graph theory tree models and mathematical models. A set of objective functions are specified in the mathematical model for the restoration of the network.;The agent platform for the fault detection is developed by Java Agent Development Framework. The restoration algorithm is programmed in MATLAB and applied to the distribution system modeled in the commercial software, Distributed Engineering Workstation and Power World Simulator. The test system in this thesis is, a distribution system developed by Southern California Edison called Circuit of the Future.;The Multi-agent system can detect the fault precisely and reconfigures the circuit using the reconfiguration algorithm. The reconfiguration will happen in a way that it always try to supply all the critical loads in the network. When there are multiple solutions available for reconfiguration, the one with good voltage profile and less power loss is selected as the solution. The algorithm makes use of shunt compensation and priority based load shedding in order to control the voltage across the network. Agents make use of learning to speed up the reconfiguration process

Energy efficient wireless sensor network protocols for monitoring and prognostics of large scale systems

In this work, energy-efficient protocols for wireless sensor networks (WSN) with applications to prognostics are investigated. Both analytical methods and verification are shown for the proposed methods via either hardware experiments or simulation. This work is presented in five papers. Energy-efficiency methods for WSN include distributed algorithms for i) optimal routing, ii) adaptive scheduling, iii) adaptive transmission power and data-rate control --Abstract, page iv

Quantitatively-Optimal Communication Protocols for Decentralized Supervisory Control of Discrete-Event Systems

In this thesis, decentralized supervisory control problems which cannot be solved without some communication among the controllers are studied. Recent work has focused on finding minimal communication sets (events or state information) required to satisfy the specifications. A quantitative analysis for the decentralized supervisory control and communication problem is pursued through which an optimal communication strategy is obtained. Finding an optimal strategy for a controller in the decentralized control setting is challenging because the best strategy depends on the choices of other controllers, all of whom are also trying to optimize their own strategies. A locally-optimal strategy is one that minimizes the cost of the communication protocol for each controller. Two important solution concepts in game theory, namely Nash equilibrium and Pareto optimality, are used to analyze optimal interactions in multi-agent systems. These concepts are adapted for the decentralized supervisory control and communication problem. A communication protocol may help to realize the exact control solution in decentralized supervisory control problem; however, the cost may be high. In certain circumstances, it can be advantageous, from a cost perspective, to reduce communication, but incur a penalty for synthesizing an approximate control solution. An exploration of the trade-off between the cost and accuracy of a decentralized discrete-event control solution with synchronously communicating controllers in a multi-objective optimization problem is presented. A widely-used evolutionary algorithm (NSGA-II) is adapted to examine the set of Pareto-optimal solutions that arise for this family of decentralized discrete-event systems (DES). The decentralized control problem is synthesized first by considering synchronous communication among the controllers. In practice, there are non-negligible delays in communication channels which lead to undesirable effects on controller decisions. Recent work on modeling communication delay between controllers only considers the case when all observations are communicated. When this condition is relaxed, it may still be possible to formulate communicating decentralized controllers that can solve the control problem with reduced communications. Instead of synthesizing reduced communication protocols under bounded delay, a procedure is developed for testing protocols designed for synchronous communications (where not all observations are communicated) for their robustness under conditions when only an upper bound for channel delay is known. Finally a decentralized discrete-event control problem is defined in timed DES (TDES) with known upper-bound for communication delay. It is shown that the TDES control problem with bounded delay communication can be converted to an equivalent problem with no delay in communication. The latter problem can be solved using the algorithms proposed for untimed DES with synchronous communication