University of South Florida

Selahattin Burak Sarsilmaz

Tansel Yucelen

Dzung Minh Duc Tran

Tyler Ray Oswald

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

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.

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Tampa, FL, United States of America

Selahattin Burak Sarsilmaz