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Found 18 papers, 2 papers with code
A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning
Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles.
A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services
As Machine Learning systems become increasingly popular across diverse application domains, including those with direct human implications, the imperative of equity and algorithmic fairness has risen to prominence in the Artificial Intelligence community.
Harnessing the Final Control Error for Optimal Data-Driven Predictive Control
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on accurate models poses many limitations in real-world applications.
Optimal Time-Invariant Distributed Formation Tracking for Second-Order Multi-Agent Systems
This paper addresses the optimal time-invariant formation tracking problem with the aim of providing a distributed solution for multi-agent systems with second-order integrator dynamics.
Adaptive Consensus-based Reference Generation for the Regulation of Open-Channel Networks
This paper deals with water management over open-channel networks (OCNs) subject to water height imbalance.
A Robustness Analysis to Structured Channel Tampering Over Secure-by-Design Consensus Networks
This work addresses multi-agent consensus networks where adverse attackers affect the convergence performances of the protocol by manipulating the edge weights.
On the Characterization of Regular Ring Lattices and their Relation with the Dirichlet Kernel
Regular ring lattices (RRLs) are defined as peculiar undirected circulant graphs constructed from a cycle graph, wherein each node is connected to pairs of neighbors that are spaced progressively in terms of vertex degree.
On the impact of regularization in data-driven predictive control
Model predictive control (MPC) is a control strategy widely used in industrial applications.
Uncertainty-aware data-driven predictive control in a stochastic setting
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant.
Distributed Dual Quaternion Based Localization of Visual Sensor Networks
In this paper we consider the localization problem for a visual sensor network.
Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent System
Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization.
On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis
For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios.
Distributed Strategies for Dynamic Coverage with Limited Sensing Capabilities
In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle structured environments without relying on metric information.
Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features
Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research.
Bearing-based Autonomous Communication Relay Positioning under Field-of-View Constraints
This paper investigates the problem of communication relay establishment for multiple agent-based mobile units using a relay vehicle.
A General Regularized Distributed Solution for System State Estimation from Relative Measurements
This work presents a novel general regularized distributed solution for the state estimation problem in networked systems.
Secure Consensus via Objective Coding: Robustness Analysis to Channel Tampering
This work mainly addresses continuous-time multiagent consensus networks where an adverse attacker affects the convergence performances of said protocol.
A Proximal Point Approach for Distributed System State Estimation
System state estimation constitutes a key problem in several applications involving multi-agent system architectures.
