1 code implementation • 7 Nov 2024 • Bowen Song, Chenxuan Wu, Andrea Iannelli
This paper revisits and extends the convergence and robustness properties of value and policy iteration algorithms for discrete-time linear quadratic regulator problems.
1 code implementation • 26 Sep 2024 • Nicolas Chatzikiriakos, Robin Strässer, Frank Allgöwer, Andrea Iannelli
In this paper we propose an end-to-end algorithm for indirect data-driven control for bilinear systems with stability guarantees.
1 code implementation • 17 Sep 2024 • Nicolas Chatzikiriakos, Andrea Iannelli
This paper considers a finite sample perspective on the problem of identifying an LTI system from a finite set of possible systems using trajectory data.
1 code implementation • 23 May 2024 • Andrea Iannelli, Romain Postoyan
When this Lyapunov function does not satisfy a designed desirable condition, an episode is triggered to update the controller gain and the corresponding Lyapunov function using the last collected data.
no code implementations • 2 Apr 2024 • Aren Karapetyan, Efe C. Balta, Anastasios Tsiamis, Andrea Iannelli, John Lygeros
Continuous-time adaptive controllers for systems with a matched uncertainty often comprise an online parameter estimator and a corresponding parameterized controller to cancel the uncertainty.
no code implementations • 15 Jan 2024 • Nicolas Chatzikiriakos, Kim P. Wabersich, Felix Berkel, Patricia Pauli, Andrea Iannelli
This combination enables us to obtain a corresponding optimal control law, which can be implemented efficiently on embedded platforms.
1 code implementation • 12 Jan 2024 • Bowen Song, Andrea Iannelli
By casting the concurrent model identification and control design as a feedback interconnection between two algorithmic systems, we provide a closed-loop analysis that shows convergence and robustness properties for arbitrary levels of excitation in the data.
no code implementations • 9 Dec 2023 • Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros
Finite-time guarantees allow the control design to distribute a limited computational budget over a time horizon and estimate the on-the-go loss in performance due to suboptimality.
no code implementations • 5 Dec 2023 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith
An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction.
no code implementations • 28 Sep 2023 • Flora Vernerey, Pierre Riedinger, Andrea Iannelli, Jamal Daafouz
This method is based on harmonic modeling and consists in converting any LTP system into an equivalent LTI system with infinite dimension.
1 code implementation • 14 Jul 2023 • 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.
no code implementations • 17 May 2023 • Aren Karapetyan, Efe C. Balta, Andrea Iannelli, John Lygeros
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 providing suboptimal solutions.
no code implementations • 29 Nov 2022 • Anilkumar Parsi, Diyou Liu, Andrea Iannelli, Roy S. Smith
Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work.
no code implementations • 14 Nov 2022 • Aren Karapetyan, Anastasios Tsiamis, Efe C. Balta, Andrea Iannelli, John Lygeros
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 online learning.
no code implementations • 20 May 2022 • Elena Arcari, Andrea Iannelli, Andrea Carron, Melanie N. Zeilinger
assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state.
no code implementations • 10 Apr 2022 • Efe C. Balta, Andrea Iannelli, Roy S. Smith, John Lygeros
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 goal of steering the system toward a desired reference trajectory.
no code implementations • 5 Apr 2022 • Anilkumar Parsi, Panagiotis Anagnostaras, Andrea Iannelli, Roy S. Smith
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances.
no code implementations • 5 Apr 2022 • Anilkumar Parsi, Andrea Iannelli, Roy S. Smith
This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order.
no code implementations • 30 Mar 2022 • Defne E. Ozan, Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories.
no code implementations • 28 Mar 2022 • Mingzhou Yin, Mehmet Tolga Akan, Andrea Iannelli, Roy S. Smith
Atomic norm regularization decomposes the transfer function into first-order atomic models and solves a group lasso problem that selects a sparse set of poles and identifies the corresponding coefficients.
no code implementations • 8 Nov 2021 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith
In this paper, confidence regions are provided for these stochastic predictors based on the prediction error distribution.
no code implementations • 13 Sep 2021 • Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli, John Lygeros, Roy S. Smith
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.
no code implementations • 10 Sep 2021 • Andrea Iannelli, Mingzhou Yin, Roy S. Smith
The paper deals with the problem of designing informative input trajectories for data-driven simulation.
no code implementations • 7 Feb 2021 • Andrea Iannelli, Urban Fasel, Roy S. Smith
The algorithm formulation hinges on the idea of replacing the orthogonal projection onto the Proper Orthogonal Decomposition modes, used in Dynamic Mode Decomposition-based approaches, with a balancing oblique projection constructed entirely from data.
no code implementations • 15 Dec 2020 • 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 data-driven simulation and control approaches.
no code implementations • 8 Dec 2020 • 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.
no code implementations • 2 Nov 2020 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith
The second one applies the signal matrix model as the predictor in predictive control.
no code implementations • 23 Aug 2020 • Arash Mehrjou, Andrea Iannelli, Bernhard Schölkopf
A coupled computational approach to simultaneously learn a vector field and the region of attraction of an equilibrium point from generated trajectories of the system is proposed.
no code implementations • 6 Jun 2020 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith
The response is estimated with an ensemble of input-output data with periodic inputs.
no code implementations • 31 Oct 2019 • Andrea Iannelli, Mohammad Khosravi, Roy S. Smith
This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty.