Control Systems and Identification

Neueste Paper

• Prediction Limits and Koopman Closure of Geometry-Induced Soft State Abstractions

  Mohit Kumar, Somayeh Kargaran · 29. September 2026

  We study when geometry-induced soft state abstractions admit accurate finite-dimensional linear dynamics. Each state is represented by simplex-valued coordinates obtained from class-specific Kernel Affine Hull Machine (KAHM) reconstruction scores, and a matrix is used to predict the next-state coord…

• Equation discovery with Bayesian tree-adjoining grammars

  Christopher A. Lindley, Nikolaos Dervilis, Keith Worden · 28. September 2026

  Tree-Adjoining Grammars (TAGs) have recently been introduced to Nonlinear System Identification (NLSI) as a means of encoding an entire model class as a finite set of grammatical rules, from which candidate models are assembled as trees. Existing TAG-based identifiers rely on evolutionary optimisati…

• Learning and Control Beyond Linearity: Towards a Non-asymptotic Theory for Bilinear Systems

  Yahya Sattar, Yassir Jedra, Robin Str\"asser, Frank Allg\"ower, Maryam Fazel, Sarah Dean · 22. September 2026

  This tutorial provides a unified view of the emerging area of bilinear learning and control. Using linear systems as a benchmark, it explains what fundamentally changes in the bilinear settings, how recent theory addresses finite-sample learning and control, and how these ideas connect to broader th…

• PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems

  Chenfeng Huang, George Michailidis · 22. September 2026

  Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system identification typically treats each system independently and cannot exploit shared structure across related systems. We propose PBML-LTI, a PAC-Bayesi…

• Learning Surrogate LPV State-Space Models with Uncertainty Quantification

  E. Javier Olucha, Amritam Das, Roland T\'oth · 21. September 2026

  The Linear Parameter-Varying (LPV) framework enables the construction of surrogate models of complex nonlinear and high-dimensional systems, facilitating efficient stability and performance analysis together with controller design. Despite significant advances in data-driven LPV modelling, existing …

• Demystifying Linear Operator Learning for Control Systems

  Max Beier, Nicolas Hoischen, Sandra Hirche, Petar Bevanda · 18. September 2026

  This paper proposes a structured approach to learning linear operators for control systems from data. We address both structural and learning-theoretic aspects of the problem. To derive structural assumptions, we propose using the well-established framework of (semi)groups for evolution equations, a…

• One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State

  Saber Salehkaleybar · 18. September 2026

  We study the problem of recovering the parameters of a multivariate Ornstein-Uhlenbeck (OU) process from steady-state observational and interventional data. In many applications, such as large-scale gene perturbation experiments, only stationary "snapshot" measurements are available, making standard…

• Non-Asymptotic Bounds for Closed-Loop Identification of Sub-Exponentially Growing Nonlinear Stochastic Systems

  Seth Siriya, Jingge Zhu, Dragan Ne\v{s}i\'c, Ye Pu · 27. August 2026

  We investigate the problem of least squares parameter estimation from single-trajectory data for discrete-time, unstable, closed-loop nonlinear stochastic systems. Specifically, we consider nonlinear systems with linearly parametrised uncertainty and additive i.i.d. process noise, in feedback with a…

• Regularised Iterative Generalised Least Squares with Optimal Selection of the Hyper-Parameter for Identifying Nonlinear Phenomenological Models

  Mark Cary, Charles Bokor · 20. August 2026

  In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from experimental data. Often the structure of such models yields fully or p…

• Sparse Orthogonal Regression Technique: A Spectral Framework for Equation Discovery, Approximation, and Integration

  Sabin Roman, Ljupco Todorovski, Saso Dzeroski · 14. August 2026

  We develop the Sparse Orthogonal Regression Technique (SORT), a sparse spectral framework for learning orthonormal-basis expansions from noisy and irregularly sampled data. SORT estimates expansion coefficients directly from observations using L1-regularized regression, avoiding explicit quadrature …

• Adaptive Symmetry Discovery for Dynamical System Identification

  Behrooz Tahmasebi, Melanie Weber · 11. August 2026

  Dynamical systems model trajectory data generated by fixed underlying dynamics, with applications ranging from biology to physics. Especially in scientific settings, dynamical systems are not generic but often exhibit symmetries imposed by physical laws, formalized through equivariance with respect …

• Spectral Distillation: From Nonlinear Dynamics to Linear State-Space Models

  Liane Galanti, Devan Shah, Shlomo Fortgang, Elad Hazan · 7. August 2026

  Can nonlinear dynamical systems be learned through a compact linear state-space representation, without directly solving a non-convex system-identification problem? We give a provable pipeline for doing so. Starting from observations of an unknown nonlinear dynamical system, we first learn an implic…

• Origins and mitigation of double descent in reduced order modeling

  Andrei A. Klishin, J. Nathan Kutz, Krithika Manohar · 30. Juli 2026

  Latent low-dimensional structure in datasets of natural and engineered systems enables their sparse sensing, or full-state reconstruction from historical data and very few carefully chosen localized measurements. Depending on the reconstruction algorithm, sensor locations, and measurement noise, the…

• A zero-one law for one-shot system identification

  Nicolas Boull\'e, Diana Halikias, Samuel E. Otto, Alex Townsend · 20. Juli 2026

  Can a model be identified from one experiment? We study analytic systems that are linearly parameterized by a combination of prescribed dictionary terms, such as partial differential operators and dynamical systems. For a single input-response pair, recovery is possible exactly when the evaluated di…

• On Regularization via Early Stopping for Least Squares Regression

  Rishi Sonthalia, Jackie Lok, Elizaveta Rebrova · 7. Juli 2026

  A fundamental problem in machine learning is understanding the effect of early stopping on the parameters obtained and the generalization capabilities of the model. Even for linear models, the effect is not fully understood for arbitrary learning rates and data. In this paper, we analyze the dynamic…

• Nonlinear Bayesian Estimator for Parameter Learning: A Fixed-Point Characterization

  Sasan Vakili, Dani\"el Woonings, Pradyumna Paruchuri, Peyman Mohajerin Esfahani · 2. Juli 2026

  This paper presents a nonlinear parameter estimator for Wiener-type state-space models obtained as a fixed-point architecture that couples two affine minimum mean-squared error (MMSE) estimators: one for the unknown parameters and one for latent variables. The architecture retains the functional str…

• Eigenspace-Based Clustering for Personalized System Identification

  Abdulmoneam Ali, Dipankar Maity, Ahmed Arafa · 23. Juni 2026

  We study the problem of system identification in heterogeneous settings, where different systems may follow distinct underlying dynamics. Existing clustered system identification approaches often rely on iterative training-based cluster assignment, which can be sensitive to learning uncertainty and …

• Integral Formulation of QENDy for Robust Nonlinear System Identification

  Nikhil Saran, Sushant Pokhriyal, Stefan Klus, Rushikesh Kamalapurkar, Joel A. Rosenfeld · 11. Juni 2026

  This manuscript proposes an integral formulation of the newly defined quadratic embedding method for identifying nonlinear systems (QENDy). In the original algorithm, trajectory data points along with their time derivatives are used. Methods for calculating time derivatives make the algorithm sensit…

• Nonlinear Estimator: Dual Bayesian Affine Estimators for Parameter Learning

  Sasan Vakili, Dani\"el Woonings, Pradyumna Paruchuri, Peyman Mohajerin Esfahani · 10. Juni 2026

  This paper presents a nonlinear parameter estimator for Wiener-type state-space models obtained as a fixed-point architecture that couples two affine minimum mean-squared error (MMSE) estimators: one for the unknown parameters and one for latent variables. The architecture retains the functional str…

• Empirical Transfer Operators and Finite-Sample Change Detection for Noisy Expanding Interval Maps

  Aparna Rajput · 8. Juni 2026

  We study finite-sample change detection for one-dimensional noisy dynamical systems using partition-based empirical approximations of stationary behaviour. Given observations from an interval-valued process, we partition the state space, estimate a finite transition matrix from observed transitions …

• Discovering Nonlinear Static Relationships in Unlabeled Dataset using Autoencoder with Ordered Variance

  Midhun T. Augustine, Parag Patil, Mani Bhushan, Sharad Bhartiya · 2. Juni 2026

  This paper presents an autoencoder with ordered variance (AEO), in which the conventional reconstruction loss is augmented by a variance-based regularization term that promotes an ordered structure within the latent space. In this structure, the latent variables are ordered by their variance compute…

• LeARN: Learnable and Adaptive Representations for Nonlinear Dynamics in System Identification

  Arunabh Singh, Joyjit Mukherjee · 2. Juni 2026

  System identification, the process of deriving mathematical models of dynamical systems from observed input-output data, has undergone a paradigm shift with the advent of learning-based methods. Addressing the intricate challenges of data-driven discovery in nonlinear dynamical systems, these method…

• A Deep State-Space Model Compression Method using Upper Bound on Output Error

  Hiroki Sakamoto, Kazuhiro Sato · 27. Mai 2026

  We study deep state-space models (Deep SSMs) that contain linear quadratic-output (LQO) systems as internal blocks and present a compression method with a provable output error guarantee. We first derive an upper bound on the output error between two Deep SSMs and show that the bound can be expresse…

• A Behavioral Framework for Data-Driven Modeling of Nonlinear Systems in Vector-Valued Reproducing Kernel Hilbert Spaces

  Boya Hou, Maxim Raginsky · 11. Mai 2026

  We generalize Jan Willems' behavioral approach to a class of discrete-time nonlinear systems in a vector-valued reproducing kernel Hilbert space (RKHS). Apart from linear time-invariant systems, this class covers nonlinear systems modeled by Volterra series and their autoregressive variants, as well…

• CLT-Optimal Parameter Error Bounds for Linear System Identification

  Yichen Zhou, Stephen Tu · 24. April 2026

  There has been remarkable progress over the past decade in establishing finite-sample, non-asymptotic bounds on recovering unknown system parameters from observed system behavior. Surprisingly, however, we show that the current state-of-the-art bounds do not accurately capture the statistical comple…