John Lygeros


Last Name

Lygeros

First Name

John

ORCID

0000-0002-6159-1962

Organisational unit

03751 - Lygeros, John / Lygeros, John

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Publications 1 - 10 of 553
  • Toward a Systems Theory of Algorithms
    Item type: Journal Article
    Dörfler, Florian; He, Zhiyu; Belgioioso, Giuseppe; et al. (2024)
    IEEE Control Systems Letters
    Traditionally, numerical algorithms are seen as isolated pieces of code confined to an in silico existence. However, this perspective is inappropriate for many modern computational approaches in control, learning, or optimization, wherein in vivo algorithms interact with their environment. Examples of such open algorithms include various real-time optimization-based control strategies, reinforcement learning, decision-making architectures, online optimization, and many more. Further, even closed algorithms in learning or optimization are increasingly abstracted in block diagrams with interacting dynamic modules and pipelines. In this opinion letter, we state our vision on a to-be-cultivated systems theory of algorithms and argue in favor of viewing algorithms as open dynamical systems interacting with other algorithms, physical systems, humans, or databases. Remarkably, the manifold tools developed under the umbrella of systems theory are well suited for addressing a rangeofchallenges in the algorithmic domain. We survey various instances where the principles of algorithmic systems theory are being developed and outline pertinent modeling, analysis, and design challenges.
  • Experimental validation of patrolling strategies in an automated surveillance environment
    Item type: Conference Paper
    Huck, Stephan M.; Kariotoglou, Nikolaos; Dahinden, Michael; et al. (2014)
    IFAC Proceedings Volumes ~ 19th IFAC World Congress, IFAC 2014. Proceedings
    The Autonomous Robotic Patrolling and Surveillance environment (AuRoPaS) is a testbed at the Automatic Control Laboratory of ETH Zurich to experimentally validate tracking, observation, and monitoring strategies for security systems. The setup comprises two high performance closed-circuit television (CCTV) cameras and mobile robots to simulate different types of surveillance scenarios. We propose a velocity based model predictive control scheme for the camera movements, which allows us to generate smooth trajectories and acquire stable images from targets. Experimental results demonstrate the successful reference tracking of the camera controller. We illustrate the integration of high level algorithms into the testbed by applying two stochastic patrolling strategies. The patrolling performances are evaluated on a scenario with moving targets visiting prioritized regions.
  • A data-driven model predictive controller to manage epidemics: The case of SARS-CoV-2 in Mauritius
    Item type: Conference Paper
    Sayed Hassen, S.Z.; Aboudonia, Ahmed; Lygeros, John (2025)
    2025 European Control Conference (ECC)
    This work investigates the benefits of implementing a systematic approach to social isolation policies during epidemics. We develop a mixed integer data-driven model predictive control (MPC) scheme based on an SIHRD model which is identified from available data. The case of the spread of the SARS-CoV-2 virus (also known as COVID-19) in Mauritius is used as a reference point with data obtained during the period December 2021 to May 2022. The isolation scheme is designed with the control decision variable taking a finite set of values corresponding to the desired level of isolation. The control input is further restricted to shifting between levels only after a minimum amount of time. The simulation results validate our design, showing that the need for hospitalisation remains within the capacity of the health centres, with the number of deaths considerably reduced by raising the level of isolation for short periods of time with negligible social and economic impact. We also show that the introduction of additional isolation levels results in a smoother containment approach with a considerably reduced hospitalisation burden.
  • A probabilistic framework for security constrained reserve scheduling of networks with wind power generation
    Item type: Conference Paper
    Vrakopoulou, Maria; Margellos, Kostas; Lygeros, John; et al. (2012)
    2012 IEEE International Energy Conference and Exhibition (ENERGYCON)
    This paper proposes a novel probabilistic framework to design an N-1 secure day-ahead dispatch, while determining the minimum cost reserves for power systems with high wind penetration. To achieve this, we build on previous work, and formulate a stochastic optimization program with chance constraints, which encode the probability of satisfying the transmission capacity constraints of the lines. To incorporate then a reserve decision scheme, we take into account the steady state behavior of the secondary frequency controller, and hence consider the reserves to be a linear function of the total generation-load mismatch. The overall problem results in a chance constrained bilinear program; to achieve tractability, two alternative convex reformulations are proposed, and the so called scenario approach is employed. This approach is based on sampling the uncertain parameter (in this paper the wind power) while keeping the desired probabilistic guarantees. To illustrate the effectiveness of the proposed technique we apply it to the IEEE 30-bus network, and compare the alternative reformulations in terms of cost and performance by means of Monte Carlo simulations, corresponding to different wind power realizations generated by a Markov chain based model.
  • Preface
    Item type: Other Conference Item
    Jadbabaie, Ali; Lygeros, John; Pappas, George J.; et al. (2021)
    Proceedings of Machine Learning Research ~ Proceedings of the 3rd Conference on Learning for Dynamics and Control
  • Semidecentralized Zeroth-Order Algorithms for Stochastic Generalized Nash Equilibrium Seeking
    Item type: Journal Article
    Zou, Suli; Lygeros, John (2023)
    IEEE Transactions on Automatic Control
    In this article, we address the problem of stochastic generalized Nash equilibrium (SGNE) seeking, where a group of noncooperative heterogeneous players aim at minimizing their expected cost under some unknown stochastic effects. Each player's strategy is constrained to a convex and compact set and should satisfy some global affine constraints. In order to decouple players' strategies under the global constraints, an extra player is introduced aiming at minimizing the violation of the coupling constraints, which transforms the original SGNE problems to extended stochastic Nash equilibrium problems. Due to the unknown stochastic effects in the objective, the gradient of the objective function is infeasible and only noisy objective values are observable. Instead of gradient-based methods, a semidecentralized zeroth-order method is developed to achieve the SGNE under a two-point gradient estimation. The convergence proof is provided for strongly monotone stochastic generalized games. We demonstrate the proposed algorithm through the Cournot model for resource allocation problems.
  • Distributed learning in the presence of disturbances
    Item type: Conference Paper
    Ramesh, Chithrupa; Schmitt, Marius; Lygeros, John (2016)
    2016 European Control Conference (ECC)
  • On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control
    Item type: Conference Paper
    Karapetyan, Aren; Balta, Efe C.; Iannelli, Andrea; et al. (2023)
    2023 62nd IEEE Conference on Decision and Control (CDC)
    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. While the asymptotic sta- bility of such algorithms is well studied, their finite-time performance has not received much attention. In this work, we quantify the performance of suboptimal linear model predictive control in terms of the additional closed-loop cost incurred due to performing only a finite number of optimization iterations. Leveraging this novel analysis framework, we propose a novel suboptimal MPC algorithm with a diminishing horizon length and finite-time closed-loop performance guarantees. This anal- ysis allows the designer to plan a limited computational power budget distribution to achieve a desired performance level. We provide numerical examples to illustrate the algorithm’s transient behavior and computational complexity.
  • Finite Sample Frequency Domain Identification
    Item type: Conference Paper
    Tsiamis, Anastasios; Abdalmoaty, Mohamed; Smith, Roy; et al. (2024)
    2024 IEEE 63rd Conference on Decision and Control (CDC)
    We study non-parametric frequency-domain system identification from a finite-sample perspective. We assume an open loop scenario where the excitation input is periodic and consider the Empirical Transfer Function Estimate (ETFE), where the goal is to estimate the frequency response at certain desired (evenly-spaced) frequencies, given input-output samples. We show that under sub-Gaussian colored noise (in time-domain) and stability assumptions, the ETFE estimates are concentrated around the true values. The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\,d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively. This rate remains valid for general irrational transfer functions and does not require a finite order state-space representation. By tuning $M$, we obtain a $N_{\mathrm{tot}}^{-1/3}$ finite-sample rate for learning the frequency response over all frequencies in the $ \mathcal{H}_{\infty}$ norm. Our result draws upon an extension of the Hanson-Wright inequality to semi-infinite matrices. We study the finite-sample behavior of ETFE in simulations.
  • Distributed collision avoidance for interacting vehicles: A command governor approach
    Item type: Conference Paper
    Tedesco, Francesco; Raimondo, Davide M.; Casavola, Alessandro; et al. (2010)
    IFAC Proceedings Volumes ~ 2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems
    This paper deals with a distributed coordination problem including collision avoidance. The problem is solved by using a Command Governor strategy based on mixed integer optimization. First, we present an algorithm to find an appropriate command in the centralized case, then a distributed sequential procedure is described. Simulations are reported for comparisons.
Publications 1 - 10 of 553