87 citations · 129 across the 19 of their papers we have counts for
25 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
Maximum Likelihood Estimation in Data-Driven Modeling and Control
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these method…
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
This paper proposes a new methodology for subspace identification of linear time-periodic (LTP) systems with periodic inputs. This method overcomes the issues related to the comput…
Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
Mingzhou Yin, Andrea Iannelli, Mohammad Khosravi +2
This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for…
Active exploration in adaptive model predictive control
Anilkumar Parsi, Andrea Iannelli, Roy S. Smith
A dual adaptive model predictive control (MPC) algorithm is presented for linear, time-invariant systems subject to bounded disturbances and parametric uncertainty in the state-spa…
Robust Adaptive Model Predictive Control with Worst-Case Cost
Anilkumar Parsi, Andrea Iannelli, Mingzhou Yin +2
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The syste…
Region of attraction analysis of nonlinear stochastic systems using Polynomial Chaos Expansion
Eva Ahbe, Andrea Iannelli, Roy S. Smith
A method is presented to estimate the region of attraction (ROA) of stochastic systems with finite second moment and uncertainty-dependent equilibria. The approach employs Polynomi…