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Florian Dörfler

Florian Dörfler
ETH Zurich | ETH Zürich · Department Information Technology and Electrical Engineering

Dipl. Ing., Ph.D.

About

221
Publications
66,846
Reads
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12,635
Citations
Introduction
Florian Dörfler currently works at the Department Information Technology and Electrical Engineering, ETH Zurich. Florian does research in Control Systems Engineering and Applied Mathematics.
Additional affiliations
July 2014 - present
ETH Zurich
Position
  • Professor (Assistant)
September 2013 - July 2014
University of California, Los Angeles
Position
  • Professor (Assistant)
June 2011 - September 2013
Los Alamos National Laboratory
Position
  • Research Intern

Publications

Publications (221)
Conference Paper
Full-text available
Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at equilibrium to build a leaner game graph. When appl...
Preprint
Willems' Fundamental Lemma provides a powerful data-driven parametrization of all trajectories of a controllable linear time-invariant system based on one trajectory with persistently exciting (PE) input. In this paper, we present a novel proof of this result which is inspired by the classical adaptive control literature and, in contrast to the exi...
Preprint
Full-text available
Design of optimal distributed linear feedback controllers to achieve a desired aggregate behavior, while simultaneously satisfying state and input constraints, is a challenging but important problem in many applications, including future power systems with weather-dependent renewable generation. System level synthesis is a recent technique which ha...
Preprint
Full-text available
Optimal linear feedback control design is a valuable but challenging problem due to nonconvexity of the underlying optimization and infinite dimensionality of the Hardy space of stabilizing controllers. A powerful class of techniques for solving optimal control problems involves using reparameterization to transform the control design to a convex b...
Preprint
Full-text available
This paper presents karma mechanisms, a novel approach to the repeated allocation of a scarce resource among competing agents over an infinite time. Examples of such resource allocation problems include deciding which trip requests to serve in a ride-hailing platform during peak demand, granting the right of way in intersections, or admitting inter...
Preprint
Full-text available
Wasserstein distributionally robust optimization has recently emerged as a powerful framework for robust estimation, enjoying good out-of-sample performance guarantees, well-understood regularization effects, and computationally tractable dual reformulations. In such framework, the estimator is obtained by minimizing the worst-case expected loss ov...
Preprint
Distributed energy storage and flexible loads are essential tools for ensuring stable and robust operation of the power grid in spite of the challenges arising from the integration of volatile renewable energy generation and increasing peak loads due to widespread electrification. This paper proposes a novel demand-side management policy to coordin...
Preprint
Full-text available
This paper presents a robust and kernelized data-enabled predictive control (RoKDeePC) algorithm to perform model-free optimal control for nonlinear systems using only input and output data. The algorithm combines robust predictive control and a non-parametric representation of nonlinear systems enabled by regularized kernel methods. The latter is...
Article
Structural balance theory characterizes stable configurations of signed interpersonal appraisal networks. Existing models explaining the convergence of appraisal networks to structural balance either diverge in finite time, or could get stuck in jammed states, or converge to only complete graphs. In this paper, we study the open problem of how non-...
Preprint
The fundamental lemma by Willems and coauthors facilitates a parameterization of all trajectories of a linear time-invariant system in terms of a single, measured one. This result plays an important role in data-driven simulation and control. Under the hood, the fundamental lemma works by applying a persistently exciting input to the system. This e...
Preprint
Full-text available
Uncertainty propagation has established itself as a fundamental area of research in all fields of science and engineering. Among its central topics stands the problem of modeling and propagating distributional uncertainty, i.e., the uncertainty about probability distributions. In this paper, we employ tools from Optimal Transport to capture distrib...
Preprint
Full-text available
This paper explores the problem of uncertainty quantification in the behavioral setting for data-driven control. Building on classical ideas from robust control, the problem is regarded as that of selecting a metric which is best suited to a data-based description of uncertainties. Leveraging on Willems' fundamental lemma, restricted behaviors are...
Preprint
Full-text available
Optimal linear feedback control design is valuable but challenging. The system level synthesis approach uses a reparameterization to expand the class of problems that can be solved using convex reformulations, among other benefits. However, to solve system level synthesis problems prior work relies on finite impulse response approximations that lea...
Preprint
Full-text available
Kron reduction is a network-reduction method that eliminates nodes with zero current injections from electrical networks operating in sinusoidal steady state. In the time domain, the state-of-the-art application of Kron reduction has been in networks with transmission lines that have constant R/L ratios. This paper considers RL networks without suc...
Preprint
Full-text available
This paper explores the stability of non-synchronous hybrid ac/dc power grids under the grid-forming hybrid angle control strategy. We formulate dynamical models for the ac grids and transmission lines, interlinking converters, and dc generations and interconnections. Next, we establish the existence and uniqueness of the closed-loop equilibria for...
Preprint
Full-text available
As we move towards safety-critical cyber-physical systems that operate in non-stationary and uncertain environments, it becomes crucial to close the gap between classical optimal control algorithms and adaptive learning-based methods. In this paper, we present an efficient optimization-based approach for computing a finite-horizon robustly safe con...
Preprint
Full-text available
The classic design of grid-forming control strategies for power converters rely on the stringent assumption of the timescale separation between DC and AC states and their corresponding control loops, e.g., AC and DC loops, power and cascaded voltage and current loops, etc. This paper proposes a multi-input multi-output based grid-forming (MIMO-GFM)...
Preprint
We present a novel grid-forming control design approach for dynamic virtual power plants. We consider a group of heterogeneous grid-forming distributed energy resources which collectively provide desired dynamic ancillary services such as fast frequency and voltage control. To achieve that, we employ an adaptive divide-and-conquer strategy which di...
Preprint
Full-text available
A central question in multi-agent strategic games deals with learning the underlying utilities driving the agents' behaviour. Motivated by the increasing availability of large data-sets, we develop an unifying data-driven technique to estimate agents' utility functions from their observed behaviour, irrespective of whether the observations correspo...
Article
Full-text available
Optimization-based control strategies are an affirmed research topic in the area of electric motors drives. These methods typically rely on an accurate parametric representation of the motor equations. In this paper, we present the transition from model-based towards data-driven optimal control strategies. We start from the model predictive control...
Preprint
Full-text available
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...
Article
The behavioral system theory and in particular a result that became known as the “fundamental lemma” give the theoretical foundation for nonparametric representations of linear time-invariant systems based on Hankel matrices constructed from data. These “data-driven” representations led in turn to new system identification, signal processing, and c...
Article
Small-signal instability of grid-connected power converters may arise when the converters use phase-locked loops (PLLs) to synchronize with a weak grid, i.e., operating in grid-following mode. Commonly, this stability problem (referred to as PLL-synchronization stability in this paper) was studied by employing a single-converter system connected to...
Article
In this paper, we present a novel control approach for dynamic virtual power plants (DVPPs). In particular, we consider a group of heterogeneous distributed energy resources (DERs) which collectively provide desired dynamic ancillary services such as fast frequency and voltage control. Our control approach relies on an adaptive divide-and-conquer s...
Article
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hierarchical priorities of each player with the inter...
Article
Full-text available
Many of today’s most used online social networks such as Instagram, YouTube, Twitter, or Twitch are based on User-Generated Content (UGC). Thanks to the integrated search engines, users of these platforms can discover and follow their peers based on the UGC and its quality. Here, we propose an untouched meritocratic approach for directed network fo...
Preprint
Full-text available
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hierarchical priorities of each player with the inter...
Article
The behavioral approach to systems theory, put forward 40 years ago by Jan C. Willems, takes a representation-free perspective of a dynamical system as a set of trajectories. Till recently, it was an unorthodox niche of research but has gained renewed interest for the newly emerged data-driven paradigm, for which it is uniquely suited due to the re...
Preprint
Full-text available
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., powerfrequency droop and matching controls. Our findings sugge...
Preprint
Full-text available
In this paper we propose an approach based on an Online Feedback Optimization (OFO) controller with grid input-output sensitivity estimation for real-time grid operation, e.g., at subsecond time scales. The OFO controller uses grid measurements as feedback to update the value of the controllable elements in the grid, and track the solution of a tim...
Preprint
Feedback optimisation is an emerging technique aiming at steering a system to an optimal steady state for a given objective function. We show that it is possible to employ this control strategy in a distributed manner. Moreover, we prove asymptotic convergence to the set of optimal configurations. To this scope, we show that exponential stability i...
Preprint
Full-text available
In this paper we propose a combined Online Feedback Optimization (OFO) and dynamic estimation approach for a real-time power grid operation under time-varying conditions. A dynamic estimation uses grid measurements to generate the information required by an OFO controller, that incrementally steers the controllable power injections set-points towar...
Preprint
Full-text available
The linear quadratic regulator (LQR) problem is a cornerstone of automatic control, and it has been widely studied in the data-driven setting. The various data-driven approaches can be classified as indirect (i.e., based on an identified model) versus direct or as robust (i.e., taking uncertainty into account) versus certainty-equivalence. Here we...
Preprint
Full-text available
The grid-forming converter is an important unit in the future power system with more inverter-interfaced generators. However, improving its performance is still a key challenge. This paper proposes a generalized architecture of the grid-forming converter from the view of multivariable feedback control. As a result, many of the existing popular cont...
Article
We study the problem of finite-time constrained optimal control of unknown stochastic linear time-invariant systems, which is the key ingredient of a predictive control algorithm - albeit typically having access to a model. We propose a novel distributionally robust data-enabled predictive control (DeePC) algorithm which uses noise-corrupted input/...
Article
Full-text available
We study the application of a data-enabled predictive control (DeePC) algorithm for position control of real-world nano-quadcopters. The DeePC algorithm is a finite-horizon, optimal control method that uses input/output measurements from the system to predict future trajectories without the need for system identification or state estimation. The al...
Technical Report
Full-text available
This thesis introduces a new variant of the recently proposed grid-forming hybrid angle control (HAC) strategy, termed arctan hybrid angle control (arctan-HAC) which ensures global asymptotic stability (GAS) of the grid-connected power converters. The standard HAC combines the dc-based matching control with a non-linear angle feedback resembling th...
Article
Inspired by classical sensitivity results for nonlinear optimization, we derive and discuss new quantitative bounds to characterize the solution map and dual variables of a parametrized nonlinear program. In particular, we derive explicit expressions for the local and global Lipschitz constants of the solution map of non-convex or convex optimizati...
Article
We employ a novel data-enabled predictive control (DeePC) algorithm in voltage source converter (VSC)-based high-voltage DC (HVDC) stations to perform safe and optimal wide-area control for power system oscillation damping. Conventional optimal wide-area control is model based. However, in practice, detailed and accurate parametric power system mod...
Article
One of the technical assumptions in the above paper is flawed and requires a global rather than a local Lipschitz condition. All of the results in the paper hold under this strengthened assumption and all relevant examples in the paper satisfy this condition.
Preprint
Full-text available
We introduce a general framework for robust data-enabled predictive control (DeePC) for linear time-invariant (LTI) systems. The proposed framework enables us to obtain model-free optimal control for LTI systems based on noisy input/output data. More specifically, robust DeePC solves a min-max optimization problem to compute the optimal control seq...
Preprint
Full-text available
This paper proposes a general framework for constructing feedback controllers that drive complex dynamical systems to "efficient" steady-state (or slowly varying) operating points. Efficiency is encoded using generalized equations which can model a broad spectrum of useful objectives, such as optimality or equilibria (e.g. Nash, Wardrop, etc.) in n...
Preprint
Mathematical optimization is one of the cornerstones of modern engineering research and practice. Yet, throughout application domains, mathematical optimization is, for the most part, considered to be a numerical discipline. Optimization problems are formulated to be solved numerically with specific algorithms running on microprocessors. An emergin...
Preprint
Full-text available
A classical approach to design controllers for interconnected systems is to assume that the different subsystems operate at different time scales, then design simpler controllers within each time scale, and finally certify stability of the interconnected system via singular perturbation analysis. In this work, we propose an alternative approach tha...
Preprint
Full-text available
We discuss connections between sequential system identification and control for linear time-invariant systems, which we term indirect data-driven control, as well as a direct data-driven control approach seeking an optimal decision compatible with recorded data assembled in a Hankel matrix and robustified through suitable regularizations. We formul...
Article
Full-text available
Data-driven control that circumvents the process of system identification by providing optimal control inputs directly from system data has attracted renewed attention in recent years. In this paper, we focus on understanding the effects of the regularization on the data-enabled predictive control (DeePC) algorithm. We provide theoretical motivatio...
Article
In this work, we present a Lyapunov framework for establishing stability with respect to a compact set for a nested interconnection of nonlinear dynamical systems ordered from slow to fast according to their convergence rates, where each of the dynamics are influenced only by the slower dynamics and the successive fastest one. The proposed approach...
Conference Paper
Full-text available
This paper presents a new grid-forming converter control strategy, termed hybrid angle control (HAC) that ensures the almost global closed-loop stability. HAC combines the dc-based matching control with a novel nonlinear angle control that resembles the droop control. The design of HAC is inspired by ideas from direct angle control and nonlinear da...
Preprint
Structural balance theory describes stable configurations of topologies of signed interpersonal appraisal networks. Existing models explaining the convergence of appraisal networks to structural balance either diverge in finite time, or could get stuck in jammed states, or converge to only complete graphs. In this paper, we study the open problem h...
Preprint
Full-text available
Data-driven control that circumvents the process of system identification by providing optimal control inputs directly from system data has attracted renewed attention in recent years. In this paper, we focus on understanding the effects of the regularization on the data-enabled predictive control (DeePC) algorithm. We provide theoretical motivatio...
Article
This paper considers the problem of online feedback optimization to solve the AC Optimal Power Flow in real-time in power grids. This consists in continuously driving the controllable power injections and loads towards the optimal set-points in time-varying conditions based on real-time measurements performed on the grid. However, instead of assumi...
Article
We consider the problem of controlling the voltage of a distribution feeder using the reactive power capabilities of inverters. On a real distribution grid, we compare the local Volt/VAr droop control recommended in recent grid codes, a centralized dispatch based on optimal power flow (OPF) programming, and a feedback optimization (FO) controller t...
Conference Paper
Full-text available
Data-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of...
Preprint
In this paper, we study the stability and convergence of continuous-time Lagrangian saddle flows to solutions of a convex constrained optimization problem. Convergence of these flows is well-known when the underlying saddle function is either strictly convex in the primal or strictly concave in the dual variables. In this paper, we show convergence...
Preprint
Full-text available
This paper introduces a new grid-forming control for power converters, termed hybrid angle control (HAC) that ensures the almost global closed-loop stability. HAC combines the recently proposed matching control with a novel nonlinear angle feedback reminiscent of (though not identical to) classic droop and dispatchable virtual oscillator controls....
Preprint
Inspired by classical sensitivity results for nonlinear optimization, we derive and discuss new quantitative bounds to characterize the solution map and dual variables of a parametrized nonlinear program. In particular, we derive explicit expressions for the local and global Lipschitz constants of the solution map of non-convex or convex optimizati...