Florian Dörfler

Last Name

Dörfler

First Name

Florian

ORCID

0000-0002-9649-5305

Organisational unit

09478 - Dörfler, Florian / Dörfler, Florian

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Publications 1 - 10 of 233

Toward a Systems Theory of Algorithms
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.
System-level performance and robustness of the grid-forming hybrid angle control
Tayyebi Khameneh, Ali; Magdaleno, Alan; Vettoretti, Denis; et al. (2022)
Electric Power Systems Research
This paper investigates the implementation and application of the multi-variable grid-forming hybrid angle control (HAC) for high-power converters in transmission grids. We explore the system-level performance and robustness of HAC concept in contrast to other grid-forming schemes i.e., power-frequency droop and matching controls. Our findings suggests that, similar to the ac-based droop control, HAC enhances the small-signal frequency stability in low-inertia power grids, and akin to the dc-based matching control, HAC exhibits robustness when accounting for the practical limits of the converter systems. Thus, HAC combines the aforementioned complementary advantageous. Furthermore, we show how retuning certain control parameters of the grid-forming controls improves the frequency performance. Last, as separate contributions, we introduce an alternative control augmentation that enhances the robustness and provide theoretical guidelines on extending the stability certificates of HAC to multi-converter systems.
Virtual Inertia Placement in Electric Power Grids
Poolla, Bala Kameshwar; Gross, Dominic; Borsche, Theodor; et al. (2018)
The IMA Volumes in Mathematics and its Applications ~ Energy Markets and Responsive Grids. Modeling, Control, and Optimization
Efficient Exploration in Continuous-time Model-based Reinforcement Learning
Treven, Lenart; Hübotter, Jonas; Sukhija, Bhavya; et al. (2024)
Advances in Neural Information Processing Systems 36
Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time. In this paper, we introduce a model-based reinforcement learning algorithm that represents continuous-time dynamics using nonlinear ordinary differential equations (ODEs). We capture epistemic uncertainty using well-calibrated probabilistic models, and use the optimistic principle for exploration. Our regret bounds surface the importance of the measurement selection strategy (MSS), since in continuous time we not only must decide how to explore, but also when to observe the underlying system. Our analysis demonstrates that the regret is sublinear when modeling ODEs with Gaussian Processes (GP) for common choices of MSS, such as equidistant sampling. Additionally, we propose an adaptive, data-dependent, practical MSS that, when combined with GP dynamics, also achieves sublinear regret with significantly fewer samples. We showcase the benefits of continuous-time modeling over its discrete-time counterpart, as well as our proposed adaptive MSS over standard baselines, on several applications.
Model-Free Nonlinear Feedback Optimization
He, Zhiyu; Bolognani, Saverio; He, Jianping; et al. (2024)
IEEE Transactions on Automatic Control
Feedback optimization is a control paradigm that enables physical systems to autonomously reach efficient operating points. Its central idea is to interconnect optimization iterations in closed-loop with the physical plant. Since iterative gradient-based methods are extensively used to achieve optimality, feedback optimization controllers typically require the knowledge of the steady-state sensitivity of the plant, which may not be easily accessible in some applications. In contrast, in this paper we develop a model-free feedback controller for efficient steady-state operation of general dynamical systems. The proposed design consists in updating control inputs via gradient estimates constructed from evaluations of the nonconvex objective at the current input and at the measured output. We study the dynamic interconnection of the proposed iterative controller with a stable nonlinear discrete-time plant. For this setup, we characterize the optimality and stability of the closed-loop behavior as functions of the problem dimension, the number of iterations, and the rate of convergence of the physical plant. To handle general constraints that affect multiple inputs, we enhance the controller with Frank-Wolfe-type updates.
Direct Adaptive Control of Grid-Connected Power Converters via Output-Feedback Data-Enabled Policy Optimization
Zhao, Feiran; Leng, Ruohan; Huang, Linbin; et al. (2025)
2025 European Control Conference (ECC)
Power electronic converters are becoming the main components of modern power systems due to the increasing integration of renewable energy sources. However, power converters may become unstable when interacting with the complex and time-varying power grid. In this paper, we propose an adaptive data-driven control method to stabilize power converters by using only online input-output data. Our contributions are threefold. First, we reformulate the output-feedback control problem as a state-feedback linear quadratic regulator (LQR) problem with a controllable non-minimal state, which can be constructed from past input-output signals. Second, we propose a data-enabled policy optimization (DeePO) method for this non-minimal realization to achieve efficient output-feedback adaptive control. Third, we use high-fidelity simulations to verify that the output-feedback DeePO can effectively stabilize grid-connected power converters and quickly adapt to the changes in the power grid.
Split-as-a-Pro: behavioral control via operator splitting and alternating projections
Tang, Yu; Cenedese, Carlo; Rimoldi, Alessio; et al. (2025)
2025 European Control Conference (ECC)
The paper introduces Split-as-a-Pro, a control framework that integrates behavioral systems theory, operator splitting methods, and alternating projection algorithms. The framework reduces dynamic optimization problems - arising in both control and estimation - to efficient projection computations. Split-as-a-Pro builds on a non-parametric formulation that exploits system structure to separate dynamic constraints imposed by individual subsystems from external ones, such as interconnection constraints and input/output constraints. This enables the use of arbitrary system representations, as long as the associated projection is efficiently computable, thereby enhancing scalability and compatibility with gray-box modeling. We demonstrate the effectiveness of Split-as-a-Pro by developing a distributed algorithm for solving finite-horizon linear quadratic control problems and illustrate its use in predictive control. Our numerical case studies show that algorithms obtained using Split-as-a-Pro significantly outperform their centralized counterparts in runtime and scalability across various standard graph topologies, while seamlessly leveraging both model-based and data-driven system representations.
A Fair and Efficient Bottleneck Congestion Management with CARMA
Cenedese, Carlo; Elokda, Ezzat; Zhang, Kenan; et al. (2025)
2025 European Control Conference (ECC)
This talk demonstrates the use of CARMA (=karma for cars) as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in the value of time (VOT), generalizing the notion of VOT to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. Commuters use karma to bid for access to the fast lane, and those who get outbid or do not participate in the scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We specialize the karma economy mean-field game model to this setting and analyze pthe roperties of its mean-field equilibrium. Unlike existing monetary schemes, CARMA is demonstrated to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. Moreover, CARMA can retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperforms tolling under a well-designed redistribution scheme.
Data-Enabled Policy Optimization for Direct Adaptive Learning of the LQR
Zhao, Feiran; Dörfler, Florian; Chiuso, Alessandro; et al. (2025)
IEEE Transactions on Automatic Control
Direct data-driven design methods for the linear quadratic regulator (LQR) mainly use offline or episodic data batches, and their online adaptation remains unclear. In this article, we propose a direct adaptive method to learn the LQR from online closed-loop data. First, we propose a new policy parameterization based on the sample covariance to formulate a direct data-driven LQR problem, which is shown to be equivalent to the certainty-equivalence LQR with optimal nonasymptotic guarantees. Second, we design a novel data-enabled policy optimization (DeePO) method to directly update the policy, where the gradient is explicitly computed using only a batch of persistently exciting (PE) data. Third, we establish its global convergence via a projected gradient dominance property. Importantly, we efficiently use DeePO to adaptively learn the LQR by performing only one-step projected gradient descent per sample of the closed-loop system, which also leads to an explicit recursive update of the policy. Under PE inputs and for bounded noise, we show that the average regret of the LQR cost is upper bounded by two terms signifying a sublinear decrease in time O(1/T−−√) plus a bias scaling inversely with signal-to-noise ratio, which are independent of the noise statistics. Finally, we perform simulations to validate the theoretical results and demonstrate the computational and sample efficiency of our method.
Dynamic Programming in Probability Spaces via Optimal Transport
Terpin, Antonio; Lanzetti, Nicolas; Dörfler, Florian (2024)
SIAM Journal on Control and Optimization
We study discrete-time finite-horizon optimal control problems in probability spaces, whereby the state of the system is a probability measure. We show that, in many instances, the solution of dynamic programming in probability spaces results from two ingredients: (i) the solution of dynamic programming in the “ground space” (i.e., the space on which the probability measures live) and (ii) the solution of an optimal transport problem. From a multi-agent control perspective, a separation principle holds: “low-level control of the agents of the fleet” (how does one reach the destination?) and “fleet-level control” (who goes where?) are decoupled.

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Florian Dörfler

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