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Finite Sample Frequency Domain Identification
The error rate is of the order of $\mathcal{O}((d_{\mathrm{u}}+\sqrt{d_{\mathrm{u}}d_{\mathrm{y}}})\sqrt{M/N_{\mathrm{tot}}})$, where $N_{\mathrm{tot}}$ is the total number of samples, $M$ is the number of desired frequencies, and $d_{\mathrm{u}},\, d_{\mathrm{y}}$ are the dimensions of the input and output signals respectively.
Small Noise Analysis of Non-Parametric Closed-Loop Identification
We revisit the problem of non-parametric closed-loop identification in frequency domain; we give a brief survey of the literature and provide a small noise analysis of the direct, indirect, and joint input-output methods when two independent experiments with identical excitation are used.
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.
Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Data
It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators.
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.
MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches
A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters.
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs
Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource.
Once upon a time step: A closed-loop approach to robust MPC design
A novel perspective on the design of robust model predictive control (MPC) methods is presented, whereby closed-loop constraint satisfaction is ensured using recursive feasibility of the MPC optimization.
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.
Diagonally Square Root Integrable Kernels in System Identification
For the stability of a Gaussian process centered at a stable impulse response, we show that the necessary and sufficient condition is the diagonally square root integrability of the corresponding kernel.
Dual adaptive MPC using an exact set-membership reformulation
Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work.
The Existence and Uniqueness of Solutions for Kernel-Based System Identification
The consequent estimation problem is well-defined under the central assumption that the convolution operators restricted to the RKHS are continuous linear functionals.
Regret Analysis of Online Gradient Descent-based Iterative Learning Control with Model Mismatch
In Iterative Learning Control (ILC), a sequence of feedforward control actions is generated at each iteration on the basis of partial model knowledge and past measurements with the goal of steering the system toward a desired reference trajectory.
Computationally efficient robust MPC using optimized constraint tightening
A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances.
Scalable tube model predictive control of uncertain linear systems using ellipsoidal sets
This contrasts with most existing tube MPC strategies using polytopic sets in the state tube, which are difficult to design and whose complexity grows combinatorially with the system order.
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.
Kernel-based Impulse Response Identification with Side-Information on Steady-State Gain
The objective function of this optimization problem is the empirical loss regularized with the norm of RKHS, and the constraint is considered for enforcing the integration of the steady-state gain side-information.
Regularized Identification with Internal Positivity Side-Information
Utilizing these conditions, the impulse response estimation problem is formulated as a constrained optimization in a reproducing kernel Hilbert space equipped with a stable kernel, and suitable constraints are imposed to encode the internal positivity side-information.
Physics-informed linear regression is competitive with two Machine Learning methods in residential building MPC
However, we also see that the physics-informed ARMAX models have a lower computational burden, and a superior sample efficiency compared to the Machine Learning based models.
A distributed framework for linear adaptive MPC
Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.
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.
Robust Adaptive Model Predictive Control of Quadrotors
To the best of our knowledge this is the first time that RAMPC has been applied in practice using a state space formulation.
The Balanced Mode Decomposition Algorithm for Data-Driven LPV Low-Order Models of Aeroservoelastic Systems
The algorithm formulation hinges on the idea of replacing the orthogonal projection onto the Proper Orthogonal Decomposition modes, used in Dynamic Mode Decomposition-based approaches, with a balancing oblique projection constructed entirely from data.
Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach
This is beneficial since measurements of the cable net form on the construction site are very expensive.
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.
Extended Full Block S-Procedure for Distributed Control of Interconnected Systems
This paper proposes a novel method for distributed controller synthesis of homogeneous interconnected systems consisting of identical subsystems.
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.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.
Robust MPC with data-driven demand forecasting for frequency regulation with heat pumps
By combining a control scheme based on Robust Model Predictive Control, with affine policies, and heating demand forecasting based on Artificial Neural Networks with online correction methods, we offer frequency regulation reserves and maintain user comfort with a system comprising a heat pump and buffer storage.
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
The response is estimated with an ensemble of input-output data with periodic inputs.
Structured exploration in the finite horizon linear quadratic dual control problem
This paper presents a novel approach to synthesize dual controllers for unknown linear time-invariant systems with the tasks of optimizing a quadratic cost while reducing the uncertainty.
