no code implementations • 27 Mar 2024 • Jair Certório, Nuno C. Martins, Richard J. La, Murat Arcak
We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium.
no code implementations • 18 Mar 2024 • Hussein Sibai, Sacha Huriot, Tyler Martin, Murat Arcak
We propose an efficient symbolic control synthesis algorithm for equivariant continuous-time dynamical systems to satisfy reach-avoid specifications.
no code implementations • 15 Dec 2023 • Alex Devonport, Peter Seiler, Murat Arcak
We then establish how an $H_\infty$ Gaussian process can serve as a prior for Bayesian system identification and as a probabilistic uncertainty model for probabilistic robust control.
no code implementations • 16 Aug 2023 • Emily Jensen, Neelay Junnarkar, Murat Arcak, Xiaofan Wu, Suat Gumussoy
This paper presents a novel framework for characterizing dissipativity of uncertain dynamical systems subject to algebraic constraints.
no code implementations • 24 Jun 2023 • Adnane Saoud, Murat Arcak
In this paper, we consider the problem of computing robust controlled invariants for discrete-time monotone dynamical systems.
no code implementations • 17 May 2023 • Baturalp Yalcin, Javad Lavaei, Murat Arcak
In this paper, we study the system identification problem for linear discrete-time systems under adversaries and analyze two lasso-type estimators.
no code implementations • 29 Nov 2022 • Alex Devonport, Peter Seiler, Murat Arcak
Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression.
no code implementations • 1 Apr 2022 • Semih Kara, Nuno C. Martins, Murat Arcak
This article proposes a methodology for such cases under the premise that a sub-strategy's duration is exponentially-distributed, leading to Erlang distributed inter-revision intervals.
1 code implementation • 31 Mar 2022 • Neelay Junnarkar, He Yin, Fangda Gu, Murat Arcak, Peter Seiler
We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network.
no code implementations • 12 Jan 2022 • Katherine S. Schweidel, He Yin, Stanley W. Smith, Murat Arcak
We present a safe-by-design trajectory planning and tracking framework for nonlinear dynamical systems using a hierarchy of system models.
no code implementations • 18 Dec 2021 • Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
no code implementations • 2 Nov 2021 • Jared Mejia, Alex Devonport, Murat Arcak
Reachability analysis is used to determine all possible states that a system acting under uncertainty may reach.
1 code implementation • 8 Sep 2021 • Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin
Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.
no code implementations • 21 Jul 2021 • Mikhail Burov, Murat Arcak, Alexander Kurzhanskiy
Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections.
no code implementations • 28 Apr 2021 • Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions.
no code implementations • 28 Apr 2021 • Alex Devonport, Adnane Saoud, Murat Arcak
Symbolic control techniques aim to satisfy complex logic specifications.
1 code implementation • 16 Dec 2020 • He Yin, Peter Seiler, Ming Jin, Murat Arcak
A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).
no code implementations • L4DC 2020 • Alex Devonport, Murat Arcak
Many practical systems are not amenable to the reachability methods that give guarantees of correctness, since they have dynamics that are strongly nonlinear, uncertain, and possibly unknown.
1 code implementation • 21 Nov 2019 • Pierre-Jean Meyer, Murat Arcak
Then we exploit these bounds and the evaluation of the first-order sensitivity matrices at a few sampled initial states to obtain an over-approximation of the first-order sensitivity, which is in turn used to over-approximate the reachable set of the initial system.
no code implementations • 29 Sep 2017 • Emmanuel Sin, Murat Arcak, Andrew Packard
We study the optimal control of an arbitrarily large constellation of small satellites operating in low Earth orbit.
Systems and Control