3 citations · 10 across the 23 of their papers we have counts for
25 papers
Online Optimization for Controlling HVAC Systems
Tong Wu, Dominik Baumann, Angela Fontan +1
Heating, ventilation, and air-conditioning (HVAC) systems account for a significant portion of buildings' energy consumption, motivating the design of energy- and cost-efficient co…
Distributed Grid Search for Hyperparameter Tuning
Constança De Herdeiro Cachim, Andrea Martin, Nicola Bastianello
The performance of many distributed optimization algorithms over peer-to-peer networks strongly depends on the choice of their hyperparameters. However, existing selection rules of…
Demonstration of Space Robot Teleoperation over a Lossy and Delayed Network using ATMOS
Inkyu Jang, Gregorio Marchesini, Nicola De Carli +15
We present a demonstration showcasing the Autonomy Testbed for Multi-purpose Orbiting Systems (ATMOS), a planar spacecraft-analog robot designed for hardware-in-the-loop evaluation…
Safe Exploration for Nonlinear Processes Using Online Gaussian Process Learning
Stefano Tonini, Soroush Rastegarpour, Hamid Reza Feyzmahdavian +2
This paper proposes a safe data-driven control framework for nonlinear systems with partially known dynamics. The method ensures stability and constraint satisfaction during online…
Detectability of Subtle Anomalies in Dynamical Systems via Log-Likelihood Ratio
Alejandro Penacho Riveiros, Matthieu Barreau, Nicola Bastianello
Industrial control applications require detecting system anomalies as accurately and quickly as possible to enable prompt maintenance. In this context, it is common to consider sev…
Model-free Anomaly Detection for Dynamical Systems with Gaussian Processes
Alejandro Penacho Riveiros, Nicola Bastianello, Matthieu Barreau
In this paper we address the problem of detecting differences or anomalies in a dynamical system, based on historical data of nominal operations. This problem encompasses quality c…
Distributed State Estimation for Discrete-Time Systems With Unknown Inputs: An Optimization Approach
Ruixuan Zhao, Guitao Yang, Nicola Bastianello +1
This paper proposes a novel Distributed Unknown Input Observer (DUIO) framework for state estimation in large-scale systems subject to local unknown inputs. We consider systems whe…
Communication-Efficient Distributed Learning with Differential Privacy
Xiaoxing Ren, Yuwen Ma, Nicola Bastianello +3
We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that is both communication-efficient and that gu…
Adaptive Online Optimization for Microgrids with Renewable Energy Sources
Wouter J. A. van Weerelt, Angela Fontan, Nicola Bastianello
In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as…
Self-Identifying Internal Model-Based Online Optimization
Wouter J. A. van Weerelt, Lantian Zhang, Silun Zhang +1
In this paper, we propose a novel online optimization algorithm built by combining ideas from control theory and system identification. The foundation of our algorithm is a control…
Communication-Efficient Learning for Satellite Constellations
Ruxandra-Stefania Tudose, Moritz H. W. Grüss, Grace Ra Kim +2
Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems…
A Communication-Efficient Decentralized Actor-Critic Algorithm
Xiaoxing Ren, Nicola Bastianello, Thomas Parisini +1
In this paper, we study the problem of reinforcement learning in multi-agent systems where communication among agents is limited. We develop a decentralized actor-critic learning f…
Physics-Informed Detection of Friction Anomalies in Satellite Reaction Wheels
Alejandro Penacho Riveiros, Nicola Bastianello, Karl H. Johansson +1
As the number of satellites in orbit has increased exponentially in recent years, ensuring their correct functionality has started to require automated methods to decrease human wo…
Formal Verification of Local Robustness of a Classification Algorithm for a Spatial Use Case
Delphine Longuet, Amira Elouazzani, Alejandro Penacho Riveiros +1
Failures in satellite components are costly and challenging to address, often requiring significant human and material resources. Embedding a hybrid AI-based system for fault detec…
Control-Based Online Distributed Optimization
Wouter J. A. van Weerelt, Nicola Bastianello
In this paper we design a novel class of online distributed optimization algorithms leveraging control theoretical techniques. We start by focusing on quadratic costs, and assuming…
Distributed Optimization and Learning for Automated Stepsize Selection with Finite Time Coordination
Apostolos I. Rikos, Nicola Bastianello, Themistoklis Charalambous +1
Distributed optimization and learning algorithms are designed to operate over large scale networks enabling processing of vast amounts of data effectively and efficiently. One of t…
Jointly Computation- and Communication-Efficient Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems over undirected networks. Specifically, we focus on designing a novel ADMM-based algorithm that is jointly computation- and communication-e…
Personalized and Resilient Distributed Learning Through Opinion Dynamics
Luca Ballotta, Nicola Bastianello, Riccardo M. G. Ferrari +1
In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of het…
Optimization and Learning in Open Multi-Agent Systems
Diego Deplano, Nicola Bastianello, Mauro Franceschelli +1
Modern artificial intelligence relies on networks of agents that collect data, process information, and exchange it with neighbors to collaboratively solve optimization and learnin…
Communication-Efficient Stochastic Distributed Learning
Xiaoxing Ren, Nicola Bastianello, Karl H. Johansson +1
We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direc…
Asynchronous Distributed Learning with Quantized Finite-Time Coordination
Nicola Bastianello, Apostolos I. Rikos, Karl H. Johansson
In this paper we address distributed learning problems over peer-to-peer networks. In particular, we focus on the challenges of quantized communications, asynchrony, and stochastic…
A survey on secure decentralized optimization and learning
Changxin Liu, Nicola Bastianello, Wei Huo +2
Decentralized optimization has become a standard paradigm for solving large-scale decision-making problems and training large machine learning models without centralizing data. How…
Learning Maximal Regions of Attraction on Compact Sets using Taylor Lyapunov Networks
Matthieu Barreau, Nicola Bastianello
We consider the problem of learning maximal Lyapunov functions to compute the maximal region of attraction of nonlinear dynamical systems using neural networks. Two obstacles limit…
Enhancing Privacy in Federated Learning through Local Training
Nicola Bastianello, Changxin Liu, Karl H. Johansson
In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowi…