Flocking of multiple autonomous agents with preserved network connectivity and heterogeneous nonlinear dynamics

Fixed-Time Cooperative Behavioral Control for Networked Autonomous Agents With Second-Order Nonlinear Dynamics

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2021.3057219 ◽

2021 ◽

pp. 1-15

Author(s):

Ning Zhou ◽

Xiaodong Cheng ◽

Zhongqi Sun ◽

Yuanqing Xia

Keyword(s):

Nonlinear Dynamics ◽

Autonomous Agents ◽

Behavioral Control ◽

Fixed Time ◽

Second Order

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Cluster-based network modeling—From snapshots to complex dynamical systems

Science Advances ◽

10.1126/sciadv.abf5006 ◽

2021 ◽

Vol 7 (25) ◽

pp. eabf5006

Author(s):

Daniel Fernex ◽

Bernd R. Noack ◽

Richard Semaan

Keyword(s):

Nonlinear Dynamics ◽

Statistical Physics ◽

Network Modeling ◽

Network Connectivity ◽

Dimensional Subspace ◽

Data Driven ◽

Lorenz Attractor ◽

Universal Method ◽

Time Resolved ◽

Kolmogorov Flow

We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning, network science, and statistical physics. CNM describes short- and long-term behavior and is fully automatable, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor, ECG heartbeat signals, Kolmogorov flow, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand, estimate, predict, and control complex systems in all scientific fields.

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A Novel Biologically Inspired Developmental Indirect Encoding for the Evolution of Neural Network Controllers for Autonomous Agents

Proceedings of the Technical University of Sofia ◽

10.47978/tus.2021.71.01.005 ◽

2021 ◽

Vol 71 (1) ◽

Author(s):

Stefan Tsokov ◽

Milena Lazarova ◽

Adelina Aleksieva-Petrova

Keyword(s):

Autonomous Agents ◽

Fitness Function ◽

Network Connectivity ◽

Network Activity ◽

Biologically Inspired ◽

Artificial Environment ◽

Third Stage ◽

Indirect Encoding ◽

Encoding Method ◽

Two Stages

Evolutionary algorithms provide the ability to automatically design robot controllers, but their wider use is hampered by a number of problems, including the difficulty of obtaining complex behaviors. This paper proposes a biologically inspired indirect encoding method for developing neural networks that control autonomous agents. The model is divided into three stages, the first two stages determine the structure of the network – the positions of the neurons and the network connectivity, and the third stage, occurring during the lifetime of the agent, determines the strength of connections based on the network activity. The model was tested experimentally by simulating an agent in an artificial environment, and the results of these simulations show that the method successfully evolved agents, capable of distinguishing between several types of objects, collecting some while avoiding others, without the use of a complex fitness function.

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