University of South Florida

United States of America As Represented by the Secretary of the Air Force

Tansel Yucelen

Selahattin Burak Sarsilmaz

Dzung Minh Duc Tran

Benjamin C Gruenwald

Jonathan A Muse

Kadriye Merve Dogan

Tyler Ray Oswald

Stefan Ristevski

Jonathan Muse

Yildiray Yildiz

Rifat Sipahi

Methods and systems for information exchange of a vehicle in a networked multiagent system are disclosed. The methods and systems include: receiving a last neighbor dataset broadcasted by a neighbor vehicle; determining a current dataset based on the last neighbor dataset and a last vehicle dataset of the first vehicle; identifying a violation of an event-triggering condition by comparing a difference between the last vehicle dataset and the current dataset with a dynamic threshold; determining a transmission dataset being associated with the current dataset; and in response to the violation, broadcasting the transmission dataset to the neighbor vehicle. The dynamic threshold is defined by an exponentially decaying term and an error between a vehicle state of the first vehicle and a reference state. Other aspects, embodiments, and features are also claimed and described.

Event triggered information exchange for distributed control of networked multiagent systems

Systems and methods are described for estimating a state of a process and an input to the process using a sensor network. Each sensor node in the sensor network is directly to one or more adjacent sensor nodes and indirectly coupled to the remaining sensor nodes through the one or more adjacent sensor nodes. Each sensor node iteratively calculates a new estimated state based on estimations of the state and the input to the process calculated by the sensor node in a previous iteration. The new estimated state is then adjusted based on a difference between a predicted and actual output of a sensor and is further adjusted based on differences between a previous estimated state calculated by the sensor node and estimated states calculated by adjacent sensor nodes.

Distributed process state and input estimation for heterogeneous active/passive sensor networks

Disclosed is a multiagent system with agents in communication with each other via a communication network. The agents have heterogeneous time-invariant dynamics such that all of the agents have a primary set of synchronization roles that are different from a secondary set of synchronization roles of a subset of the agents.

Distributed control of multiagent systems with heterogeneity in synchronization roles

Disclosed is a multiagent system having multiple agents in communication with each other via a communication network. The agents can be tasked with performing a global objective. At least a subset of the agents can also be tasked with performing a local objective in addition to the global objective. Performance of the local objective can occur without deteriorating performance of the global objective.

Control of multiagent systems with local and global objectives

Various examples are provided related to adaptive architectures for controlling uncertain system with unmodeled dynamics. A closed-loop dynamical system subject to an adaptive controller can remain stable if there does not exist significant unmodeled dynamics or the effect of system uncertainties is negligible. In one example, a system includes a controller that can receive one or more input signals including control signals, sensor data associated with operation of the aircraft or aviation system, or a combination thereof; generate an system control signal utilizing a model reference adaptive control architecture comprising an adaptive robustifying term that maintains system stability within defined bounds; and provide the system control signal to an actuator or other system components to adjust operation of, e.g., an aircraft or aviation system.

Adaptive architecture for controlling uncertain systems with unmodeled dynamics

The present disclosure describes an actuator system comprising an actuator unit that is configured to be positioned next to a structure; and an adaptive controller unit that is configured to receive a command input for the actuator unit and output an actuator command based on a reference model of a physical system that includes the actuator unit and the structure, wherein the actuator command does not alter trajectories of the reference model. In various embodiments, the uncertain dynamical system of the physical system is augmented with the actuator dynamics to provide improved stability.

Adaptive control mechanism for uncertain systems with actuator dynamics

Systems and methods for controlling motion of a vehicle in a group of vehicles. In one embodiment, the system includes a communication interface, a vehicle platform for travelling among the group of vehicles, and an electronic processor. The electronic processor is configured to determine a local virtual tracking error signal and a controller state signal. The electronic processor is also configured to determine a self-navigation input control signal based on the local virtual tracking error signal and the controller state signal. The self-navigation input control signal is for navigating the vehicle platform. A trajectory of an exosystem is based on a boundedness condition. The vehicle communicates with other vehicles in the group of vehicles via a fixed augmented directed connected communication graph topology. Each vehicle in the group of vehicles is stabilizable and satisfies a transmission zero condition. Design matrices of the vehicle satisfy an internal model principle.

Distributed control of heterogeneous multi-agent systems

A method and system for distributed spatial control of a formation of vehicles includes receiving at a first formation vehicle via a peer-to-peer communication interface, direction of travel and formation density information that indicate a course of travel for the first vehicle while travelling as a member of the formation of vehicles. The peer-to-peer formation density information indicates a distance to maintain from other neighboring formation vehicles. A formation vehicle self-navigation command is generated for navigating the first vehicle when travelling in one dimensional, two dimensional, or three dimensional space as a member of the formation of vehicles. The self-navigation command is based on the peer-to-peer direction of travel and formation density information. The direction of travel information is based on locally determined spatial relationships of a portion of the formation of vehicles.

Tools and methods for distributed spatial control of swarms via multiplex information networks

Systems and methods for implementing and/or validating a model reference adaptive control (MRAC) for human-in-the-loop control of a vehicle system. A first operator model is applied to a first feedback-loop-based MRAC scheme, wherein the first operator model is configured to adjust a control command provided as an input to the MRAC scheme based at least in part on an actual action of the vehicle system and a reference action for the vehicle system with a time-delay. A stability limit of a first operating parameter is determined for the MRAC scheme based on the application of the first operator model to the first feedback-loop-based MRAC scheme. The MRAC scheme is validated in response to determining that expected operating conditions of the first operating parameter are within the determined stability limit of the first operating parameter.

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Tampa, FL, United States of America

Tansel Yucelen