Division of Decision and Control Systems/Department of Intelligent Systems/School of Electrical Engineering and Computer Science KTH Royal Institute of Technology
AIMD-Inspired Switching Control of Computing Networks Eleftherios Vlahakis, Raphaël Jungers, Nikolaos Athanasopoulos, Seán McLoone IEEE Transactions on Control of Network Systems, 2024 We consider the scheduling problem of requests entering a distributed computing network consisting of a set of noncooperative nodes, where a node is represented by a queue combined with a computing unit. Our interaction-free setup between nodes renders decentralized scheduling challenging, with most existing results focusing on centralized or static solutions. Inspired by congestion control, we propose a new average-based additive increase multiplicative decrease (AIMD) admission control policy, which requires minimal communication between individual nodes and an aggregator. The proposed admission policy infers a discrete-event model expressed as a positive, constrained switching system that is triggered whenever the queue of the aggregation point of requests vanishes. We show convergence of the proposed AIMD system under unknown, peak-bounded workload profiles by analyzing the spectrum of rank-one perturbations of symmetric matrices and the boundedness of the joint spectral radius of sets of symmetric matrices. Contrary to methods that address scheduling and resource allocation asynchronously or via a two-step approach, our AIMD-based scheme can tackle both tasks simultaneously. This is illustrated by proposing a decentralized resource allocation controller coupled with the scheduling scheme leading to a stable closed-loop control system that is guaranteed to avoid underutilization of resources and is tunable via the sets of AIMD parameters.
Distributed Sequential Receding Horizon Control of Multi-Agent Systems Under Recurring Signal Temporal Logic Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas 2024 European Control Conference Ecc 2024, 2024 We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task.
Probabilistic Tube-based Control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis, Dimos V. Dimarogonas Proceedings of the IEEE Conference on Decision and Control, 2024 We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical.
Conformal Prediction for Distribution-Free Optimal Control of Linear Stochastic Systems Eleftherios E. Vlahakis, Lars Lindemann, Pantelis Sopasakis, Dimos V. Dimarogonas IEEE Control Systems Letters, 2024 We address an optimal control problem for linear stochastic systems with unknown noise distributions and joint chance constraints using conformal prediction. Our approach involves designing a feedback controller to maintain an error system within a prediction region (PR). We define PRs as sublevel sets of a nonconformity score over error trajectories, enabling the handling of joint chance constraints. We propose two methods to design feedback control and PRs: one through direct optimization over error trajectory samples, and the other indirectly using the S-procedure with a disturbance ellipsoid obtained from data. By tightening constraints with PRs, we solve a relaxed problem to synthesize a feedback policy. Our method ensures reliable probabilistic guarantees based on marginal coverage, independent of data size.
Distributionally Robust Control for Chance-Constrained Signal Temporal Logic Specifications Arash Bahari Kordabad, Eleftherios E. Vlahakis, Lars Lindemann, Dimos V. Dimarogonas, Sadegh Soudjani Proceedings of the IEEE Conference on Decision and Control, 2024 We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method.
Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications M. H. W. Engelaar, Z. Zhang, E. E. Vlahakis, D.V. Dimarogonas, M. Lazar, et al. Proceedings of the IEEE Conference on Decision and Control, 2024 This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with realtime allocation of signal temporal logic (STL) specifications. Based on previous work, we decompose specifications into subspecifications on the individual agent level. To leverage the efficiency of task allocation, a heuristic filter evaluates potential task allocation based on STL robustness, and subsequently, an auctioning algorithm determines the definitive allocation of specifications. Finally, a control strategy is synthesized for each agent-specification pair using tube-based model predictive control (MPC), ensuring provable probabilistic satisfaction. We demonstrate the efficacy of the proposed methods using a multi-shuttle scenario that highlights a promising extension to automated driving applications like vehicle routing.
Distributed Resource Autoscaling in Kubernetes Edge Clusters Dimitrios Spatharakis, Ioannis Dimolitsas, Eleftherios Vlahakis, Dimitrios Dechouniotis, Nikolaos Athanasopoulos, et al. Proceedings of the 2022 18th International Conference of Network and Service Management Intelligent Management of Disruptive Network Technologies and Services Cnsm 2022, 2022
Multi-Agent Temporal Logic Planning via Penalty Functions and Block-Coordinate Optimization EE Vlahakis, AB Kordabad, L Lindemann, P Sopasakis, S Soudjani, ... arXiv preprint arXiv:2602.17434 , 2026 2026
Distribution-Free Stochastic MPC for Joint-in-Time Chance-Constrained Linear Systems L Vogel, A Carron, EE Vlahakis, DV Dimarogonas arXiv preprint arXiv:2512.10738 , 2025 2025
Conformal data-driven control of stochastic multi-agent systems under collaborative signal temporal logic specifications EE Vlahakis, L Lindemann, DV Dimarogonas 2025 IEEE 64th Conference on Decision and Control (CDC), 624-629 , 2025 2025 Citations: 4
State dependent disturbances in computation of forward reachable sets and minimal invariant sets N Athanasopoulos, E Vlahakis, C Townsend, S Olaru 2025 Citations: 2
Efficient coordination and synchronization of multi-robot systems under recurring linear temporal logic D Peron, VN Fernandez-Ayala, EE Vlahakis, DV Dimarogonas 2025 IEEE International Conference on Robotics and Automation (ICRA), 10194 … , 2025 2025 Citations: 3
Data-driven distributionally robust control for interacting agents under logical constraints AB Kordabad, EE Vlahakis, L Lindemann, S Gros, DV Dimarogonas, ... arXiv preprint arXiv:2503.09816 , 2025 2025 Citations: 7
Data-Driven Distributionally Robust Control for Interacting Agents under Logical Constraints A Bahari Kordabad, EE Vlahakis, L Lindemann, S Gros, DV Dimarogonas, ... arXiv e-prints, arXiv: 2503.09816 , 2025 2025
Probabilistic tube-based control synthesis of stochastic multi-agent systems under signal temporal logic EE Vlahakis, L Lindemann, P Sopasakis, DV Dimarogonas 2024 IEEE 63rd Conference on Decision and Control (CDC), 1586-1592 , 2024 2024 Citations: 13
Risk-aware real-time task allocation for stochastic multi-agent systems under STL specifications MHW Engelaar, Z Zhang, EE Vlahakis, DV Dimarogonas, M Lazar, ... 2024 IEEE 63rd Conference on Decision and Control (CDC), 8213-8218 , 2024 2024 Citations: 4
Conformal prediction for distribution-free optimal control of linear stochastic systems EE Vlahakis, L Lindemann, P Sopasakis, DV Dimarogonas IEEE Control Systems Letters 8, 2835-2840 , 2024 2024 Citations: 10
Distributed sequential receding horizon control of multi-agent systems under recurring signal temporal logic EE Vlahakis, L Lindemann, DV Dimarogonas 2024 European Control Conference (ECC), 305-310 , 2024 2024 Citations: 6
Robust Invariant Sets for Systems Affected by State-Dependent Disturbance C Townsend, S Olaru, N Athanasopoulos, E Vlahakis 2024 Citations: 2
Distributionally Robust Control for Chance-Constrained Signal Temporal Logic Specifications A Bahari Kordabad, EE Vlahakis, L Lindemann, DV Dimarogonas, ... 2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 1593-1598 , 2024 2024
AIMD-inspired switching control of computing networks E Vlahakis, R Jungers, N Athanasopoulos, S McLoone IEEE Transactions on Control of Network Systems 11 (2), 683-695 , 2023 2023 Citations: 2
Quantifying Impact on Safety from Cyber-Attacks on Cyber-Physical Systems E Vlahakis, G Provan, G Werner, S Yang, N Athanasopoulos 22nd IFAC World Congress Yokohama, Japan , 2023 2023 Citations: 11
Distributed resource autoscaling in kubernetes edge clusters D Spatharakis, I Dimolitsas, E Vlahakis, D Dechouniotis, ... 2022 18th International Conference on Network and Service Management (CNSM … , 2022 2022 Citations: 19
Model-matching methods and distributed control of networks consisting of a class of heterogeneous dynamic agents EE Vlahakis, GD Halikias International Journal of Control 95 (8), 2066-2082 , 2022 2022
Optimal resource scheduling and allocation in distributed computing systems W Ren, E Vlahakis, N Athanasopoulos, R Jungers 2022 American Control Conference (ACC), 2327-2332 , 2022 2022 Citations: 5
Optimal Resource Scheduling and Allocation under Allowable Over-Scheduling W Ren, E Vlahakis, N Athanasopoulos, RM Jungers arXiv preprint arXiv:2204.00038 , 2022 2022
Modelling issues and aggressive robust load frequency control of interconnected electric power systems L Dritsas, E Kontouras, E Vlahakis, I Kitsios, G Halikias, A Tzes International Journal of Control 95 (3), 753-767 , 2022 2022 Citations: 29
MOST CITED SCHOLAR PUBLICATIONS
Distributed LQR design for a class of large-scale multi-area power systems E Vlahakis, L Dritsas, G Halikias Energies 12 (14), 2664 , 2019 2019 Citations: 32
Modelling issues and aggressive robust load frequency control of interconnected electric power systems L Dritsas, E Kontouras, E Vlahakis, I Kitsios, G Halikias, A Tzes International Journal of Control 95 (3), 753-767 , 2022 2022 Citations: 29
Distributed resource autoscaling in kubernetes edge clusters D Spatharakis, I Dimolitsas, E Vlahakis, D Dechouniotis, ... 2022 18th International Conference on Network and Service Management (CNSM … , 2022 2022 Citations: 19
Distributed LQR design for identical dynamically coupled systems: Application to Load Frequency Control of multi-area Power Grid EE Vlahakis, LD Dritsas, GD Halikias 2019 IEEE 58th Conference on Decision and Control (CDC), 4471-4476 , 2019 2019 Citations: 16
Distributed LQR methods for networks of non-identical plants EE Vlahakis, GD Halikias 2018 IEEE conference on decision and control (cdc), 6145-6150 , 2018 2018 Citations: 16
Probabilistic tube-based control synthesis of stochastic multi-agent systems under signal temporal logic EE Vlahakis, L Lindemann, P Sopasakis, DV Dimarogonas 2024 IEEE 63rd Conference on Decision and Control (CDC), 1586-1592 , 2024 2024 Citations: 13
Temperature and concentration control of exothermic chemical processes in continuous stirred tank reactors E Vlahakis, G Halikias Transactions of the Institute of Measurement and Control 41 (15), 4274-4284 , 2019 2019 Citations: 13
Quantifying Impact on Safety from Cyber-Attacks on Cyber-Physical Systems E Vlahakis, G Provan, G Werner, S Yang, N Athanasopoulos 22nd IFAC World Congress Yokohama, Japan , 2023 2023 Citations: 11
Distributed LQR-based suboptimal control for coupled linear systems EE Vlahakis, LD Dritsas, GD Halikias IFAC-PapersOnLine 52 (20), 109-114 , 2019 2019 Citations: 11
Conformal prediction for distribution-free optimal control of linear stochastic systems EE Vlahakis, L Lindemann, P Sopasakis, DV Dimarogonas IEEE Control Systems Letters 8, 2835-2840 , 2024 2024 Citations: 10
AIMD scheduling and resource allocation in distributed computing systems E Vlahakis, N Athanasopoulos, S McLoone 2021 60th IEEE Conference on Decision and Control (CDC), 4642-4647 , 2021 2021 Citations: 9
Data-driven distributionally robust control for interacting agents under logical constraints AB Kordabad, EE Vlahakis, L Lindemann, S Gros, DV Dimarogonas, ... arXiv preprint arXiv:2503.09816 , 2025 2025 Citations: 7
Model-Matching type-methods and Stability of Networks consisting of non-Identical Dynamic Agents EE Vlahakis, GD Halikias IFAC-PapersOnLine 51 (23), 426-431 , 2018 2018 Citations: 7
Distributed sequential receding horizon control of multi-agent systems under recurring signal temporal logic EE Vlahakis, L Lindemann, DV Dimarogonas 2024 European Control Conference (ECC), 305-310 , 2024 2024 Citations: 6
Distributed model predictive load frequency control of multi-area power grid: A decoupling approach EE Vlahakis, LD Dritsas, GD Halikias IFAC-PapersOnLine 52 (20), 205-210 , 2019 2019 Citations: 6
Optimal resource scheduling and allocation in distributed computing systems W Ren, E Vlahakis, N Athanasopoulos, R Jungers 2022 American Control Conference (ACC), 2327-2332 , 2022 2022 Citations: 5
Cooperative distributed LQR control for longitudinal flight of a formation of non-identical low-speed experimental UAV's EE Vlahakis, E Milonidis, GD Halikias 2018 UKACC 12th International Conference on Control (CONTROL), 295-300 , 2018 2018 Citations: 5
Conformal data-driven control of stochastic multi-agent systems under collaborative signal temporal logic specifications EE Vlahakis, L Lindemann, DV Dimarogonas 2025 IEEE 64th Conference on Decision and Control (CDC), 624-629 , 2025 2025 Citations: 4
Risk-aware real-time task allocation for stochastic multi-agent systems under STL specifications MHW Engelaar, Z Zhang, EE Vlahakis, DV Dimarogonas, M Lazar, ... 2024 IEEE 63rd Conference on Decision and Control (CDC), 8213-8218 , 2024 2024 Citations: 4
Distributed optimal and predictive control methods for networks of dynamic systems EE Vlahakis City, University of London , 2020 2020 Citations: 4