no code implementations • 1 Apr 2024 • Anastasios Tsiamis, Mohamed Abdalmoaty, Roy S. Smith, John Lygeros

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.

no code implementations • 23 Mar 2024 • Mohamed Abdalmoaty, Roy S. Smith

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.

no code implementations • 22 Mar 2024 • Roy S. Smith, Mohamed Abdalmoaty, Mingzhou Yin

Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system.

no code implementations • 9 Mar 2024 • Mohamed Abdalmoaty, Efe C. Balta, John Lygeros, Roy S. Smith

It is well known that ignoring the presence of stochastic disturbances in the identification of stochastic Wiener models leads to asymptotically biased estimators.

no code implementations • 25 Dec 2023 • Mohamed Abdalmoaty, Jared Miller, Mingzhou Yin, Roy S. Smith

We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data.

no code implementations • 5 Dec 2023 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith

An initial condition estimator is proposed by filtering the measurements with the one-step-ahead stochastic data-driven prediction.

no code implementations • 29 Apr 2023 • Verena Häberle, Linbin Huang, Xiuqiang He, Eduardo Prieto-Araujo, Roy S. Smith, Florian Dörfler

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.

no code implementations • 27 Apr 2023 • Varsha Behrunani, Hanmin Cai, Philipp Heer, Roy S. Smith, John Lygeros

Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource.

no code implementations • 20 Mar 2023 • Anilkumar Parsi, Marcell Bartos, Amber Srivastava, Sebastien Gros, Roy S. Smith

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.

no code implementations • 17 Mar 2023 • Mingzhou Yin, Roy S. Smith

The kernel-based method has been successfully applied in linear system identification using stable kernel designs.

no code implementations • 24 Feb 2023 • Mohammad Khosravi, Roy S. Smith

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.

no code implementations • 29 Nov 2022 • Anilkumar Parsi, Diyou Liu, Andrea Iannelli, Roy S. Smith

Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work.

no code implementations • 17 Apr 2022 • Mohammad Khosravi, Roy S. Smith

The consequent estimation problem is well-defined under the central assumption that the convolution operators restricted to the RKHS are continuous linear functionals.

no code implementations • 10 Apr 2022 • Efe C. Balta, Andrea Iannelli, Roy S. Smith, John Lygeros

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.

no code implementations • 5 Apr 2022 • Anilkumar Parsi, Panagiotis Anagnostaras, Andrea Iannelli, Roy S. Smith

A robust model predictive control (MPC) method is presented for linear, time-invariant systems affected by bounded additive disturbances.

no code implementations • 5 Apr 2022 • Anilkumar Parsi, Andrea Iannelli, Roy S. Smith

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.

no code implementations • 30 Mar 2022 • Defne E. Ozan, Mingzhou Yin, Andrea Iannelli, Roy S. Smith

Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories.

no code implementations • 28 Mar 2022 • Mingzhou Yin, Mehmet Tolga Akan, Andrea Iannelli, Roy S. Smith

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.

no code implementations • 8 Nov 2021 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith

In this paper, confidence regions are provided for these stochastic predictors based on the prediction error distribution.

no code implementations • 31 Oct 2021 • Mohammad Khosravi, Roy S. Smith

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.

no code implementations • 31 Oct 2021 • Mohammad Khosravi, Roy S. Smith

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.

no code implementations • 29 Oct 2021 • Felix Bünning, Benjamin Huber, Adrian Schalbetter, Ahmed Aboudonia, Mathias Hudoba de Badyn, Philipp Heer, Roy S. Smith, John Lygeros

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.

no code implementations • 13 Sep 2021 • Anilkumar Parsi, Ahmed Aboudonia, Andrea Iannelli, John Lygeros, Roy S. Smith

Adaptive model predictive control (MPC) robustly ensures safety while reducing uncertainty during operation.

no code implementations • 10 Sep 2021 • Andrea Iannelli, Mingzhou Yin, Roy S. Smith

The paper deals with the problem of designing informative input trajectories for data-driven simulation.

no code implementations • 26 Feb 2021 • Alexandre Didier, Anilkumar Parsi, Jeremy Coulson, Roy S. Smith

To the best of our knowledge this is the first time that RAMPC has been applied in practice using a state space formulation.

no code implementations • 7 Feb 2021 • Andrea Iannelli, Urban Fasel, Roy S. Smith

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.

no code implementations • 20 Dec 2020 • Yvonne R. Stürz, Mohammad Khosravi, Roy S. Smith

This is beneficial since measurements of the cable net form on the construction site are very expensive.

no code implementations • 15 Dec 2020 • Andrea Iannelli, Mingzhou Yin, Roy S. Smith

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.

no code implementations • 14 Dec 2020 • Mingzhou Yin, Roy S. Smith

The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure.

no code implementations • 11 Dec 2020 • Giulia De Pasquale, Yvonne R. Sturz, Maria Elena Valcher, Roy S. Smith

This paper proposes a novel method for distributed controller synthesis of homogeneous interconnected systems consisting of identical subsystems.

no code implementations • 8 Dec 2020 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith

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.

no code implementations • 2 Nov 2020 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith

The second one applies the signal matrix model as the predictor in predictive control.

no code implementations • 28 Oct 2020 • Christopher König, Mohammad Khosravi, Markus Maier, Roy S. Smith, Alisa Rupenyan, John Lygeros

This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization.

no code implementations • 15 Sep 2020 • Felix Bünning, Joseph Warrington, Philipp Heer, Roy S. Smith, John Lygeros

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.

no code implementations • 6 Jun 2020 • Mingzhou Yin, Andrea Iannelli, Roy S. Smith

The response is estimated with an ensemble of input-output data with periodic inputs.

no code implementations • 31 Oct 2019 • Andrea Iannelli, Mohammad Khosravi, Roy S. Smith

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.

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