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Found 12 papers, 1 papers with code
Bridging Transient and Steady-State Performance in Voltage Control: A Reinforcement Learning Approach with Safe Gradient Flow
Deep reinforcement learning approaches are becoming appealing for the design of nonlinear controllers for voltage control problems, but the lack of stability guarantees hinders their deployment in real-world scenarios.
Learning Provably Stable Local Volt/Var Controllers for Efficient Network Operation
We identify the conditions on the surrogates and control parameters to ensure that the locally acting controllers collectively converge, in a global asymptotic sense, to a DN operating point agreeing with the local surrogates.
Learning Local Volt/Var Controllers Towards Efficient Network Operation with Stability Guarantees
This paper considers the problem of voltage regulation in distribution networks.
Temporal Forward-Backward Consistency, Not Residual Error, Measures the Prediction Accuracy of Extended Dynamic Mode Decomposition
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of functions.
Stable Reinforcement Learning for Optimal Frequency Control: A Distributed Averaging-Based Integral Approach
Specifically, we use RL to learn a neural network-based control policy mapping from the integral variables of DAI to the controllable power injections which provides optimal transient frequency control, while DAI inherently ensures the frequency restoration and optimal economic dispatch.
Selective Inhibition and Recruitment of Linear-Threshold Thalamocortical Networks
Neuroscientific evidence shows that for most brain networks all pathways between cortical regions either pass through the thalamus or a transthalamic parallel route exists for any direct corticocortical connection.
Generalizing Dynamic Mode Decomposition: Balancing Accuracy and Expressiveness in Koopman Approximations
This paper tackles the data-driven approximation of unknown dynamical systems using Koopman-operator methods.
Learning Barrier Functions with Memory for Robust Safe Navigation
Control barrier functions are widely used to enforce safety properties in robot motion planning and control.
Motion Planning
Robotics
Parallel Learning of Koopman Eigenfunctions and Invariant Subspaces For Accurate Long-Term Prediction
We identify conditions on the network topology to ensure the algorithm identifies the maximal Koopman-invariant subspace in the span of the original dictionary, characterize its time, computational, and communication complexity, and establish its robustness against communication failures.
Dynamics of Data-driven Ambiguity Sets for Hyperbolic Conservation Laws with Uncertain Inputs
This study focuses on the latter step by investigating the spatio-temporal evolution of data-driven ambiguity sets and their associated guarantees when the random QoIs they describe obey hyperbolic partial-differential equations with random inputs.
Optimization and Control Analysis of PDEs
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part II: Multi-Layer Dynamics and Top-Down Recruitment
Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition.
Hierarchical Selective Recruitment in Linear-Threshold Brain Networks, Part I: Single-Layer Dynamics and Selective Inhibition
Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions.
