Search Results for author: Murat Arcak

Found 20 papers, 4 papers with code

Incentive Designs for Learning Agents to Stabilize Coupled Exogenous Systems

no code implementations27 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.

Symmetry-based Abstraction Algorithm for Accelerating Symbolic Control Synthesis

no code implementations18 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.

Position

Frequency-domain Gaussian Process Models for $H_\infty$ Uncertainties

no code implementations15 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.

Gaussian Processes

Certifying Stability and Performance of Uncertain Differential-Algebraic Systems: A Dissipativity Framework

no code implementations16 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.

Characterization, Verification and Computation of Robust Controlled Invariants for Monotone Dynamical Systems

no code implementations24 Jun 2023 Adnane Saoud, Murat Arcak

In this paper, we consider the problem of computing robust controlled invariants for discrete-time monotone dynamical systems.

Exact Recovery for System Identification with More Corrupt Data than Clean Data

no code implementations17 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.

Frequency Domain Gaussian Process Models for $H^\infty$ Uncertainties

no code implementations29 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.

Gaussian Processes regression

Population Games With Erlang Clocks: Convergence to Nash Equilibria For Pairwise Comparison Dynamics

no code implementations1 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.

valid

Synthesis of Stabilizing Recurrent Equilibrium Network Controllers

1 code implementation31 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.

Policy Gradient Methods

Safe-by-Design Planner-Tracker Synthesis

no code implementations12 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.

Model Predictive Control Trajectory Planning

Data-Driven Reachability analysis and Support set Estimation with Christoffel Functions

no code implementations18 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.

DaDRA: A Python Library for Data-Driven Reachability Analysis

no code implementations2 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.

Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems

1 code implementation8 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.

LEMMA

Speed Advisory System Using Real-Time Actuated Traffic Light Phase Length Prediction

no code implementations21 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.

Data-Driven Reachability Analysis with Christoffel Functions

no code implementations28 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.

Symbolic Abstractions From Data: A PAC Learning Approach

no code implementations28 Apr 2021 Alex Devonport, Adnane Saoud, Murat Arcak

Symbolic control techniques aim to satisfy complex logic specifications.

PAC learning

Imitation Learning with Stability and Safety Guarantees

1 code implementation16 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).

Imitation Learning

Estimating Reachable Sets with Scenario Optimization

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.

Interval Reachability Analysis using Second-Order Sensitivity

1 code implementation21 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.

Small Satellite Constellation Separation using Linear Programming based Differential Drag Commands

no code implementations29 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

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