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Results for “Optimal Control”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Optimal control of connected automated vehicles with event/self-triggered control barrier functions
Not Available
Supervisory Optimal Control for Photovoltaics Connected to an Electric Power Grid.
Abstract not provided.
The Optimal Control with Implicit Phase Coordination of a Collective of Wind Turbines.
Abstract not provided.
Optimal Control for the Quantum Simulation of Nuclear Dynamics
We propose a method for enacting the unitary time propagation of two interacting neutrons at leading order of chiral effective-field theory by efficiently encoding the nuclear dynamics into a single multilevel quantum device. The emulated output of the quantum simulation shows that, by applying a single gate that draws on the underlying characteristics of the device, it is possible to observe multiple cycles of the nuclear dynamics before the onset of decoherence. Owing to the signal's longevity, we can then extract spectroscopic properties of the simulated nuclear system. This allows us to validate the encoding of the nuclear Hamiltonian and the robustness of the simulation in the presence of quantum-hardware noise by comparing the extracted spectroscopic information to exact calculations. This work paves the way for transformative calculations of the dynamical properties of nuclei on near-term quantum devices.
From Pulses to Circuits and Back Again: A Quantum Optimal Control Perspective on Variational Quantum Algorithms
Not Available
Optimal Control of Differentially Private EV Charging: A Scalable Learning Approach Under Uncertainty
Internet of Things (IoT)-enabled electric vehicles (IoEVs) enable intelligent charging coordination that accounts for grid congestion. However, increased data exchange raises privacy concerns, as charging patterns can reveal sensitive driver behavior to grid operators. Here, we propose a differentially private (DP) EV charging framework that enables coordinated control while protecting driver data with theoretical privacy guarantees. Nevertheless, integrating DP inevitably introduces uncertainty into the control strategy for EVs, which can lead to infeasible solutions. To tackle this challenge, we develop a feasible and scalable control algorithm based on constrained reinforcement learning (CRL) and convex hulls. While our framework is designed to handle the uncertainty introduced by DP, it is general and also applicable to other sources of uncertainty in EV charging, such as the stochastic nature of driver behavior and renewable variability. This ensures feasible and privacy-preserving coordination of EV charging at scale. Our method constructs convex hulls within the action space to guarantee feasibility under stochastic constraints and incorporates constraint reduction techniques to improve scalability. Case studies based on IEEE benchmark systems demonstrate that the proposed approach effectively balances feasibility under uncertainty, scalability, and privacy in large-scale EV charging control.
Sum of Squares based Convex Approach for Optimal Control Synthesis.
Abstract not provided.
Optimal Control of a Battery Energy Storage System with a Charge-Temperature-Health Model.
Abstract not provided.
Optimized Control of Distribution Switches to Balance a Low Cost Photovoltaic Microgrid.
Abstract not provided.
Quandary: An open-source C++ package for high-performance optimal control of open quantum systems
Explore the source record for details and available documents.
A Neural Network Approach for High-Dimensional Optimal Control Applied to Multiagent Path Finding
Not provided.