87 citations · 129 across the 19 of their papers we have counts for
32 papers
A Unified Bayesian Framework for Data-Driven Smoothing, Prediction, and Control
Mingzhou Yin, Andrea Iannelli, Seyed Ali Nazari +1
Extending data-driven algorithms based on Willems' fundamental lemma to stochastic data often requires empirical and customized workarounds. This work presents a unified Bayesian f…
Distributed Dual Quaternion Extended Kalman Filtering for Spacecraft Pose Estimation
Mathias Hudoba de Badyn, Jonas Binz, Andrea Iannelli +1
In this paper, a distributed dual-quaternion multiplicative extended Kalman filter for the estimation of poses and velocities of individual satellites in a fleet of spacecraft is a…
Stochastic Data-Driven Predictive Control: Regularization, Estimation, and Constraint Tightening
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control wi…
An Online Learning Analysis of Minimax Adaptive Control
Venkatraman Renganathan, Andrea Iannelli, Anders Rantzer
We present an online learning analysis of minimax adaptive control for the case where the uncertainty includes a finite set of linear dynamical systems. Precisely, for each system…
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
Aren Karapetyan, Efe C. Balta, Andrea Iannelli +1
Inexact methods for model predictive control (MPC), such as real-time iterative schemes or time-distributed optimization, alleviate the computational burden of exact MPC by providi…
A Dual System-Level Parameterization for Identification from Closed-Loop Data
Amber Srivastava, Mingzhou Yin, Andrea Iannelli +1
This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabiliz…
Dual adaptive MPC using an exact set-membership reformulation
Anilkumar Parsi, Diyou Liu, Andrea Iannelli +1
Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformula…
Implications of Regret on Stability of Linear Dynamical Systems
Aren Karapetyan, Anastasios Tsiamis, Efe C. Balta +2
The setting of an agent making decisions under uncertainty and under dynamic constraints is common for the fields of optimal control, reinforcement learning, and recently also for…
A quantitative and constructive proof of Willems' Fundamental Lemma and its implications
Julian Berberich, Andrea Iannelli, Alberto Padoan +3
Willems' Fundamental Lemma provides a powerful data-driven parametrization of all trajectories of a controllable linear time-invariant system based on one trajectory with persisten…
Stochastic MPC with robustness to bounded parametric uncertainty
Elena Arcari, Andrea Iannelli, Andrea Carron +1
The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robust…
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
Efe C. Balta, Andrea Iannelli, Roy S. Smith +1
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the…
Computationally efficient robust MPC using optimized constraint tightening
Anilkumar Parsi, Panagiotis Anagnostaras, Andrea Iannelli +1
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances. The main contribution is the offline desig…
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
Anilkumar Parsi, Andrea Iannelli, Roy S. Smith
This work proposes a novel robust model predictive control (MPC) algorithm for linear systems affected by dynamic model uncertainty and exogenous disturbances. The uncertainty is m…
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models
Defne E. Ozan, Mingzhou Yin, Andrea Iannelli +1
Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories. Systems controll…
On the Regret of Control
Aren Karapetyan, Andrea Iannelli, John Lygeros
The synthesis approach is a cornerstone robust control design technique, but is known to be conservative in some cases. The objective of this paper is to qua…
Infinite-Dimensional Sparse Learning in Linear System Identification
Mingzhou Yin, Mehmet Tolga Akan, Andrea Iannelli +1
Regularized methods have been widely applied to system identification problems without known model structures. This paper proposes an infinite-dimensional sparse learning algorithm…
An explicit dual control approach for constrained reference tracking of uncertain linear systems
Anilkumar Parsi, Andrea Iannelli, Roy S. Smith
A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. A s…
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Recently, direct data-driven prediction has found important applications for controlling unknown systems, particularly in predictive control. Such an approach provides exact predic…
Design of input for data-driven simulation with Hankel and Page matrices
Andrea Iannelli, Mingzhou Yin, Roy S. Smith
The paper deals with the problem of designing informative input trajectories for data-driven simulation. First, the excitation requirements in the case of noise-free data are discu…
A distributed framework for linear adaptive MPC
Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli +2
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation. In this paper, a distributed version is proposed to deal with network s…
Decentralized trajectory optimization for multi-agent exploration
Dimitris Gkouletsos, Andrea Iannelli, Mathias Hudoba de Badyn +1
Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of…
The Balanced Mode Decomposition Algorithm for Data-Driven LPV Low-Order Models of Aeroservoelastic Systems
Andrea Iannelli, Urban Fasel, Roy S. Smith
A novel approach to reduced-order modeling of high-dimensional time varying systems is proposed. It leverages the formalism of the Dynamic Mode Decomposition technique together wit…
Experiment design for impulse response identification with signal matrix models
Andrea Iannelli, Mingzhou Yin, Roy S. Smith
This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for da…
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matri…