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Found 17 papers, 0 papers with code
Human-Variability-Respecting Optimal Control for Physical Human-Machine Interaction
Physical Human-Machine Interaction plays a pivotal role in facilitating collaboration across various domains.
Passivation of Clustered DC Microgrids with Non-Monotone Loads
In this paper, we consider the problem of voltage stability in DC networks containing uncertain loads with non-monotone incremental impedances and where the steady-state power availability is restricted to a subset of the buses in the network.
Socially Integrated Navigation: A Social Acting Robot with Deep Reinforcement Learning
In addition, we propose a novel socially integrated navigation approach where the robot's social behavior is adaptive and emerges from the interaction with humans.
ReACT: Reinforcement Learning for Controller Parametrization using B-Spline Geometries
We focus on the control of parameter-variant systems, a class of systems with complex behavior which depends on the operating conditions.
Passivity-based economic ports for optimal operation of networked DC microgrids
This interconnection scheme requires only an exchange of the local prices and allows a globally economic optimal operation of networked microgrids at steady state, while guaranteeing asymptotic stability of the networked microgrids via the passivity properties of economic ports.
Cooperative Decision-Making in Shared Spaces: Making Urban Traffic Safer through Human-Machine Cooperation
The novelties of this paper are the adaptation of a general cooperative and shared control framework to this novel use case and the application of an explicit prediction model of the pedestrian.
A Unifying Passivity-Based Framework for Pressure and Volume Flow Rate Control in District Heating Networks
A fundamental precondition for the secure and efficient operation of district heating networks (DHNs) is a stable hydraulic behavior.
On MPC-based Strategies for Optimal Voltage References in DC Microgrids
Modern power systems are characterized by low inertia and fast voltage dynamics due to the increase of sources connecting via power electronics and the removal of large traditional thermal generators.
Port-Hamiltonian Modelling for Analysis and Control of Gas Networks
The proposed pipeline and network models can serve as a basis for passivity-based control and analysis while the power system parallels facilitate the transfer of existing methods.
Passivity-based power sharing and voltage regulation in DC microgrids with unactuated buses
In this paper, we propose a novel four-stage distributed controller for a DC microgrid that achieves power sharing and average voltage regulation for the voltages at actuated and unactuated buses.
Toward Transactive Control of Coupled Electric Power and District Heating Networks
Transactive control has been developed as a promising approach based on market and control mechanisms to coordinate supply and demand in energy systems, which when applied to power systems is being referred to as transactive energy.
A Unified Passivity-Based Framework for Control of Modular Islanded AC Microgrids
Voltage and frequency control in an islanded AC microgrid (ImGs) amount to stabilizing an a priori unknown ImG equilibrium induced by loads and changes in topology.
Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System
Therefore, we design an ADP-based optimal trajectory tracking controller and apply it to a large-scale ball-on-plate system.
Deep Decentralized Reinforcement Learning for Cooperative Control
In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters
We conclude our paper with an example which demonstrates that our new method successfully learns the optimal tracking controller and outperforms existing approaches in terms of tracking error and cost.
Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains
Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents.
Adaptive Optimal Control for Reference Tracking Independent of Exo-System Dynamics
This paper provides for the first time an adaptive optimal control method capable to track reference trajectories not being generated by a time-invariant exo-system.
