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Found 13 papers, 0 papers with code
Optimal Data-Driven Prediction and Predictive Control using Signal Matrix Models
Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system.
Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization
We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data.
Stochastic Data-Driven Predictive Control: Regularization, Estimation, and Constraint Tightening
An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction.
Error Bounds for Kernel-Based Linear System Identification with Unknown Hyperparameters
The kernel-based method has been successfully applied in linear system identification using stable kernel designs.
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models
Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories.
Infinite-Dimensional Sparse Learning in Linear System Identification
Atomic norm regularization decomposes the transfer function into first-order atomic models and solves a group lasso problem that selects a sparse set of poles and identifies the corresponding coefficients.
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
In this paper, confidence regions are provided for these stochastic predictors based on the prediction error distribution.
Design of input for data-driven simulation with Hankel and Page matrices
The paper deals with the problem of designing informative input trajectories for data-driven simulation.
Experiment design for impulse response identification with signal matrix models
This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for data-driven simulation and control approaches.
On Low-Rank Hankel Matrix Denoising
The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure.
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data.
Maximum Likelihood Estimation in Data-Driven Modeling and Control
The second one applies the signal matrix model as the predictor in predictive control.
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
The response is estimated with an ensemble of input-output data with periodic inputs.
