17 citations · 32 across the 20 of their papers we have counts for
11 papers · 1 filter
Joint State-Parameter Inference Enhances Estimation Performance in Model-Based Digital Therapeutics for Type 1 Diabetes
Milad Banitalebi Dehkordi, Vihangkumar V. Naik, Manas Mejari +2
Blood glucose estimation is the cornerstone of model-based decision support (DS) and Automated Insulin Delivery (AID) systems. Control systems that rely on physiologic/compartmenta…
Learning reduced-order latent linear models for Kalman filtering of nonlinear systems
Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga
We propose a filtering-oriented end-to-end learning framework to identify reduced-order models explicitly tailored for state estimation in high-dimensional nonlinear systems. An au…
Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation
Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga
We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization techni…
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione +3
We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robot…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
Alessandro Colombo, Riccardo Busetto, Valentina Breschi +3
Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system n…
One controller to rule them all
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as…
Bias correction and instrumental variables for direct data-driven model-reference control
Manas Mejari, Valentina Breschi, Simone Formentin +1
Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI)…
In-context learning of state estimators
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation i…
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
Manas Mejari, Ankit Gupta, Dario Piga
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (L…
From system models to class models: An in-context learning paradigm
Marco Forgione, Filippo Pura, Dario Piga
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same cla…