Pith. sign in

REVIEW 4 major objections 5 minor 36 references

Learning the Integral Quadratic Constraints on Plant-Model Mismatch

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that plant-model mismatch can be captured as a learned integral quadratic constraint, with a one-class SVM recovering an accurate frequency-domain description from sampled trajectories.

desk verdict A plausible data-driven IQC learning method with a real finite-horizon-to-infinite-horizon gap; the numerics are suggestive but not a proof. read the letter →

arxiv 2502.00976 v1 pith:WBCDACSM submitted 2025-02-03 eess.SY cs.SY

Wentao Tang This is my paper · ORCID
classification eess.SYcs.SY
keywords integralquadraticconstraintsplant-modelmismatchone-classSVMdata-drivencontrolrobustdissipativitylearningfrequency-domainidentification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper makes the case that the gap between an unknown nonlinear plant and a linear nominal model can be captured, for robust-control purposes, by an integral quadratic constraint (IQC) whose parameters are learned from input–output trajectories. It proposes to fix a bank of dynamic filters, write the hard IQC inequality as a linear constraint on a symmetric matrix $M$, and estimate $M$ using a one-class SVM that maximizes the viability margin. On a delay-mismatch example and on a nonlinear two-phase reactor, the learned IQC recovers the expected frequency-domain uncertainty profile. This matters because a valid IQC is the kind of certificate that allows robust controller synthesis to remain within the linear framework.

What carries the argument

The central object is the pair $(\Psi, M)$: a fixed dynamic multiplier $\Psi$ (a bank of stable rational filters) extracts features $z = \Psi(w, v)$ from the mismatch input $v$ and output $w$, and the symmetric matrix $M$ defines the quadratic supply rate $z^\top M z$. The load-bearing identity is the equivalence between the hard time-domain inequality $\int_0^T z^\top M z\, dt \ge 0$ for all finite horizons (dissipativity under the multiplier) and its soft frequency-domain counterpart under Parseval, so that learning $M$ from finite-horizon samples is offered as a route to a frequency-domain IQC certificate. The learning itself is carried out by a one-class SVM in which each sample trajectory is represented by its Gram-like matrix $\Gamma = \int_0^T z z^\top\, dt$.

What would settle it

Run a long-horizon simulation of a known nonlinear plant with a dense-frequency multi-tone input, compute $\int_0^T z^\top M z\, dt$ for the learned $M$, and evaluate the frequency-domain integral $\int_{-\infty}^{\infty} [y^\dagger\ u^\dagger] \Pi(j\omega) [y; u]\, d\omega$ on the same signals; the claim collapses if a fresh sample violates the hard inequality by more than the SVM margin, or if the soft IQC integral is negative.

Watch

Extended reading notes

Core claim

The central claim is that the matrix $M$ in the hard IQC inequality $\langle M,\Gamma\rangle \ge 0$ can be learned from sampled trajectories and that the resulting IQC provides an accurate frequency-domain description of the plant-model mismatch. The paper frames the learning problem as: given fixed filters $\Psi$, each trajectory yields a positive semidefinite 'dual dissipativity parameter' $\Gamma = \int_0^T z(t) z^\top(t)\, dt$, and one seeks $M$, block-diagonal with negative-definite output block and positive-definite input block, such that $\langle M,\Gamma\rangle \ge 0$ for all samples. This is cast as a one-class SVM with margin $\rho$ and slack variables, and the paper relies on the standard generalization bound of SVMs to argue that a sufficiently large sample set gives a small probability of violating the constraint on new trajectories. The examples indicate that the learned IQC overestimates the true mismatch except near the theoretical roll-off, and that filter choice (e.g., Butterworth instead of simple first-order) tightens the fit.

Load-bearing premise

The leap is that checking the quadratic inequality on a finite set of sampled trajectories, with inputs chosen to cover the frequency range of interest, is enough to certify an infinite-horizon frequency-domain IQC for the unknown plant.

Editorial extensions

If this is right

  • The learned IQC provides a frequency-domain bound on the mismatch that can feed directly into robust stability analysis and robust controller synthesis with the nominal linear model.
  • With sufficiently many informative trajectories, the OC-SVM generalization bound bounds the probability that a new trajectory violates the learned constraint, giving a statistical certificate for the estimated matrix $M$.
  • The accuracy and tightness of the learned IQC depend on the user's choice of filter structure; matched filters (e.g., Butterworth with the correct cutoff) markedly improve the recovery, as shown on both examples.
  • The same learning procedure works for multiplicative mismatch ($y - y_0 = \Delta y_0$) by redefining the mismatch input, extending the approach beyond the additive residual used in the reactor example.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural downstream test the paper does not run is plugging the learned IQC into a robust controller synthesis and benchmarking closed-loop performance against a nominal-only controller; the examples stop at showing the shape of the learned bound.
  • The finite-horizon-to-frequency-domain step could be probed by placing a persistent sinusoid between the sampled frequency grid points and checking whether the soft IQC on the true plant stays nonnegative; Remark 5 assumes this coverage rather than proving it.
  • Because the SVM regularizes $\|M\|_F$ uniformly across filters, a frequency-weighted variant of the objective could trade off tightness at control-relevant frequencies against overall coverage, something the paper does not explore.
  • The paper fixes the nominal model by a step response; using closed-loop operating data would bring actuator limits and estimator dynamics into the mismatch, making the excitation-coverage assumption harder to satisfy in practice.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper proposes a data-driven method for learning an integral quadratic constraint (IQC) that characterizes plant-model mismatch between an unknown nonlinear plant and a linear nominal model. The method fixes a dynamic multiplier consisting of a bank of stable filters and uses one-class support vector machines (OC-SVM) to find the dissipativity matrix M, requiring that sampled finite-horizon trajectories satisfy the inequality ⟨M, Γ(i)⟩ ≥ ρ, with soft margins. A generalization bound from Schölkopf et al. is cited. The approach is illustrated on a time-delay mismatch example, where the learned frequency-domain multiplier is compared with a theoretical majorant, and on a two-phase reactor example with a nonlinear plant, where the learned mismatch curve is compared with selected sinusoidal responses.

Significance. If the learned IQC could be certified as a valid frequency-domain description of the mismatch, the method would enable data-driven robust controller synthesis for nonlinear plant-model mismatch while remaining in a linear control framework. The OC-SVM formulation is clean and the optimization problem (5) is well posed; the author provides code and uses standard numerical tools. However, the central claim that the learned IQC 'provides an accurate description of the plant-model mismatch on the frequency domain' (Section V) is not supported by the theoretical results or the empirical procedure. The generalization bound applies to a user-chosen sampling distribution of finite-horizon trajectories, not to all L2 signals or to the infinite-horizon frequency-domain IQC. As a result, the significance of the contribution in its current form is limited: it is a heuristic identification scheme that would need additional guarantees or a substantial repositioning of claims to serve as a robust-control certificate.

major comments (4)
  1. [Section II, Definition 1 and Section III-B, Eq. (5)] The learned matrix M is obtained by requiring ⟨M, Γ(i)⟩ ≥ ρ − ξ_i on m sampled finite-horizon trajectories. The generalization bound in Theorem 2 only controls the probability that a future trajectory drawn from the same sampling distribution violates this inequality. In contrast, Definition 1 requires the inequality to hold for every trajectory and every time interval [t0, t1], and the frequency-domain IQC (2) requires an infinite-horizon L2 condition. No argument is provided that the finite-sample OC-SVM solution extends to a valid IQC over all L2 signals. This is the load-bearing gap that connects the learned matrix to the claimed frequency-domain characterization, and it is not addressed in the manuscript.
  2. [Section III-B, Remark 4 and Section III-C, Theorem 2] The soft OC-SVM formulation explicitly allows violations of the margin (ν > 0 and ξ_i > 0), and the reported simulations show nonzero average violations. Theorem 2 provides a probabilistic bound on the measure of future trajectories under the sampling distribution, but it does not provide a worst-case guarantee over all admissible inputs. Since robust control certificates require universal validity of the IQC, the softness of the learned constraint prevents the learned M from being used as a formal certificate of robustness, even if the sampling distribution is informative. The paper does not discuss this limitation in the context of controller synthesis.
  3. [Section IV, Fig. 2 and filter selection] The paper initially uses simple filters (φ1 = 1/(s+1), φ2 = s/(s+1), φ3 = φ1φ2) and reports a learned ℓ(jω) ≈ 4ω²/(1+ω²), which is then compared with the theoretical ℓ0(jω). After observing the pole misalignment, second-order Butterworth filters with cutoff frequency π/2θ0 are introduced to obtain a 'more accurate' result, with the admission that 'this assumes prior knowledge on a better pole assignment and better filter choice.' Because the filter set is adjusted after seeing the target curve, the comparison is not an independent validation of the learning method; it demonstrates the sensitivity of the result to a user-chosen feature set rather than the recovery of the true IQC.
  4. [Section V, Fig. 5 and concluding paragraph] The validation in the two-phase reactor example compares the learned ℓ(jω) with only five sinusoidal responses (ωτ0 = 0.03, 0.3, 3, 30) and relies on a visual inspection. This does not test the inequality ⟨M, Γ⟩ ≥ 0 on independent random trajectories or for generic L2 inputs, nor does it provide a quantitative measure of how well the learned IQC bounds the actual mismatch. The statement that 'the proposed IQC learning approach indeed provides an accurate description of the plant-model mismatch on the frequency domain' is therefore stronger than the evidence supports.
minor comments (5)
  1. [Section III-B, Eq. (5)] The role of the parameter ν is unusual: in standard OC-SVM, ν sets an upper bound on the fraction of outliers, while here it appears as a penalty weight 1/(νm) in the objective. The paper should clarify the interpretation of ν and its effect on the learned solution.
  2. [Section IV, paragraph after Fig. 2] The sentence 'For simplicity, let τ ≡ 1 be fixed and ψ be learned from data' appears to contain a typo; it should refer to the matrix M (or the function ℓ) rather than 'ψ'.
  3. [Section III-C, Theorem 2] The statement of Theorem 2 uses δ both as the confidence parameter on the left and inside the bound on the right, which is confusing; the bound should be restated with distinct variables (e.g., 'with probability at least 1−δ') and the dependence on m, ϵ, and the covering number should be made explicit.
  4. [Section V, filter construction] The choice of the nine frequencies ω1, ..., ω9 and the construction of the filter bank are described only briefly; the paper should discuss how robust the learned ℓ(jω) is to this choice, since the method's success appears to depend on the user's selection of filters.
  5. [Section IV, numerical details] The reported matrix M is displayed as a full 3×3 matrix but is summarized as diag(0,4,0); this is an approximation that should be explained, as the off-diagonal entries are not negligible for the subsequent frequency-domain computation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the learned IQC is validated on external benchmarks, not on its own fitting constraints.

full rationale

The optimization (5) fits M to finite-horizon sampled Gramians Gamma_i, but the paper's evidence for accurate frequency-domain recovery is independent of those constraints. For the delay example, the learned l(jw) is compared to the theoretical majorant l0 from Megretski and Rantzer [21]; for the reactor, the conclusion is checked against time-domain simulations at selected frequencies. Thus the fitted matrix is not being used as its own certificate. The self-citations ([27]-[29], [36]) supply the OC-SVM/dissipativity-learning toolbox and are not the basis for the IQC-specific claim; the generalization bound in Theorem 2 is quoted from Scholkopf et al. [34] and LoCicero [30]. The finite-horizon-to-infinite-horizon step in Remark 5 (sinusoidal sampling covering the frequency range) is asserted rather than proved, and the soft-margin formulation allows violations, so the method does not establish a worst-case hard IQC in the sense of Definition 1; however, this is a gap between the fitted object and a robust-control certificate, not a circularity, because no equation in the paper defines the target frequency-domain IQC in terms of the training data.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The approach fits the matrix M and several user-chosen hyperparameters. No new physical entities are introduced. The key unproven assumption is the finite-horizon to frequency-domain bridge.

free parameters (5)
  • Dissipativity matrix M = M ≈ diag(-1, 0, 4, 0) in Example IV; different in reactor (not tabulated)
    The central fitted object; chosen by OC-SVM to satisfy sampled inequalities.
  • Softness constant ν = ν = 0.01, 0.05 in Example IV; varied in Example V
    User-chosen hyperparameter controlling the penalty on margin violations.
  • Definiteness margins ε_w, ε_v = unspecified
    Ad hoc constants enforcing strict block definiteness of M.
  • Filter poles and filter selection = poles at 1 for φ1 and φ2; Butterworth cutoff π/2θ0; reactor frequencies 10^-1 to 10^1
    User-chosen dynamic multiplier; accuracy depends critically on these (paper admits in §IV).
  • Sampling design = m=500, A=1 or 1/4, log10 ω uniform on [-2,2], T=45 min in reactor
    Number of trajectories, input amplitude, frequency range, and duration are chosen by the experimenter.
assumptions (5)
  • domain assumption The plant-model mismatch Δ satisfies an IQC of the form (Ψ, M) with block-diagonal M and the pre-specified filters Ψ.
    Invoked in §III-A when fixing Ψ and estimating M; if false, the learned constraint is not a genuine IQC for the mismatch.
  • domain assumption Sampled trajectories are i.i.d. draws from a fixed signal distribution P, so the OC-SVM generalization bound (Theorem 2) applies.
    Needed for the statistical guarantees; not verified for deterministic plant experiments.
  • domain assumption The plant starts at the origin and is noiseless; nonzero initial states and disturbances are only absorbed by the soft margin.
    Stated in Remark 4 as the justification for the soft OC-SVM formulation.
  • ad hoc to paper Satisfying the hard IQC on finitely many sampled trajectories implies a valid soft IQC on the frequency domain for the true plant.
    This unproven leap connects the fitted M to a frequency-domain certificate; it is only heuristically supported by the sampling recommendation in Remark 5.
  • ad hoc to paper The chosen finite filter set is rich enough to represent the true frequency-domain IQC within the approximation tolerance.
    Filter richness is acknowledged as critical in Remark 3 and §IV, but no quantitative richness condition is given.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Learning the Integral Quadratic Constraints on Plant-Model Mismatch." pith.science (2026). https://pith.science/paper/WBCDACSM

@misc{pith2026250200976,
  author       = {Pith},
  title        = {Pith review of: Learning the Integral Quadratic Constraints on Plant-Model Mismatch},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WBCDACSM}},
  note         = {Machine review of arXiv:2502.00976}
}
read the original abstract

While a characterization of plant-model mismatch is necessary for robust control, the mismatch usually can not be described accurately due to the lack of knowledge about the plant model or the complexity of nonlinear plants. Hence, this paper considers this problem in a data-driven way, where the mismatch is captured by parametric forms of integral quadratic constraints (IQCs) and the parameters contained in the IQC equalities are learned from sampled trajectories from the plant. To this end, a one-class support vector machine (OC-SVM) formulation is proposed, and its generalization performance is analyzed based on the statistical learning theory. The proposed approach is demonstrated by a single-input-single-output time delay mismatch and a nonlinear two-phase reactor with a linear nominal model, showing accurate recovery of frequency-domain uncertainties.

Figures

Figures reproduced from arXiv: 2502.00976 by the authors.

Figure 1
Figure 1. The plant and dynamic multiplier The remaining paper is organized as follows. Preliminaries on nonlinear control theory is provided in §II. The proposed technique is then discussed in §III. A simple numerical example and a practical application are shown in §IV and §V, respectively1 . Conclusions are given in §VI. Notations. Upper case letters are used to represent ma￾trices, transfer functions, or dynamical systems… view at source ↗
Figure 2
Figure 2. Frequency in the learned IQC of the example system [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Frequency response ℓ(jω) in the learned IQC for the two-phase reactor. for a duration of 45 min, where log10 ωτ0 comes from a uniform distribution on [−2, 2] and A = 1/4. Under these settings, the simulations are numerically stable. To learn the IQC, we choose the IQC structure as in (6) with τ (jω) ≡ 1 and M = diag(1, Mu). Thus, ℓ(jω) = Ψu(jω) †MuΨu(jω). The filters in Ψu(s) are decided in the following way: (i) 9 … view at source ↗
Figures from the paper (2 more)
Figure 3
Figure 3. Figure 3: The two-phase reactor. for a tighter estimation, we set φ1 as the second Butterworth filter with cutoff frequency π/2θ0, and φ2 as its high-pass counterpart. The resulting ℓ is shown in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 5
Figure 5. Figure 5: Comparison of actual and nominal responses under [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 35 canonical work pages

  1. [1]

    J. B. Rawlings, D. Q. Mayne, and M. Diehl, Model predictive control: Theory, computation, and design . Nob Hill, 2nd ed., 2018

    work page 2018

  2. [2]

    Closed-loop issues in system identification,

    P. Van den Hof, “Closed-loop issues in system identification,” Ann. Rev. Control, vol. 22, pp. 173–186, 1998

    work page 1998

  3. [3]

    Quantifying the impact of model-plant mismatch on controller performance,

    A. S. Badwe, R. S. Patwardhan, S. L. Shah, S. C. Patwardhan, and R. D. Gudi, “Quantifying the impact of model-plant mismatch on controller performance,” J. Process Control , vol. 20, no. 4, pp. 408– 425, 2010

    work page 2010

  4. [4]

    Control performance monitoring – a review and assess- ment,

    S. J. Qin, “Control performance monitoring – a review and assess- ment,” Comput. Chem. Eng. , vol. 23, no. 2, pp. 173–186, 1998

    work page 1998

  5. [5]

    Recent developments in multivariable controller performance monitoring,

    S. J. Qin and J. Yu, “Recent developments in multivariable controller performance monitoring,” J. Process Control, vol. 17, no. 3, pp. 221– 227, 2007

    work page 2007

  6. [6]

    Huang and R

    B. Huang and R. Kadali, Dynamic modeling, predictive control and performance monitoring: A data-driven subspace approach . Springer, 2008

    work page 2008

  7. [7]

    Improved methodology and set-point design for diagnosis of model-plant mismatch in control loops using plant-model ratio,

    S. Kaw, A. K. Tangirala, and A. Karimi, “Improved methodology and set-point design for diagnosis of model-plant mismatch in control loops using plant-model ratio,” J. Process Control , vol. 24, no. 11, pp. 1720–1732, 2014

    work page 2014

  8. [8]

    A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era,

    X. Gao, F. Yang, C. Shang, and D. Huang, “A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era,” Chin. J. Chem. Eng. , vol. 24, no. 8, pp. 952–962, 2016

    work page 2016

Show all 36 references
  1. [9]

    D. M. Prett and M. Morari, The Shell process control workshop . Butterworth, 1987

    1987

  2. [10]

    Automatic decomposition of large-scale industrial processes for dis- tributed MPC on the Shell-Yokogawa Platform for Advanced Control and Estimation (PACE),

    W. Tang, P. Carrette, Y . Cai, J. M. Williamson, and P. Daoutidis, “Automatic decomposition of large-scale industrial processes for dis- tributed MPC on the Shell-Yokogawa Platform for Advanced Control and Estimation (PACE),” Comput. Chem. Eng. , vol. 178, p. 108382, 2023

    2023

  3. [11]

    Detection of model-plant mismatch in MPC applications,

    A. S. Badwe, R. D. Gudi, R. S. Patwardhan, S. L. Shah, and S. C. Pat- wardhan, “Detection of model-plant mismatch in MPC applications,” J. Process Control, vol. 19, no. 8, pp. 1305–1313, 2009

    2009

  4. [12]

    Detection of model-plant mismatch in closed-loop control system,

    D. Ling, Y . Zheng, H. Zhang, W. Yang, and B. Tao, “Detection of model-plant mismatch in closed-loop control system,” J. Process Control, vol. 57, pp. 66–79, 2017

    2017

  5. [13]

    A framework of hybrid model develop- ment with identification of plant-model mismatch,

    Y . Chen and M. Ierapetritou, “A framework of hybrid model develop- ment with identification of plant-model mismatch,” AIChE J., vol. 66, no. 10, p. e16996, 2020

    2020

  6. [14]

    Online detection of model-plant mismatch in closed-loop systems with gaussian processes,

    Q. Wu and W. Du, “Online detection of model-plant mismatch in closed-loop systems with gaussian processes,” IEEE Trans. Ind. Inform., vol. 18, no. 4, pp. 2213–2222, 2021

    2021

  7. [15]

    Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control,

    S. H. Son, J. W. Kim, T. H. Oh, D. H. Jeong, and J. M. Lee, “Learning of model-plant mismatch map via neural network modeling and its application to offset-free model predictive control,” J. Process Control, vol. 115, pp. 112–122, 2022

    2022

  8. [16]

    Physics-informed neural networks for process systems: Handling plant-model mismatch,

    F. Moayedi, A. Chandrasekar, S. Rasmussen, S. Sarna, B. Corbett, and P. Mhaskar, “Physics-informed neural networks for process systems: Handling plant-model mismatch,” Ind. Eng. Chem. Res. , vol. 63, no. 31, pp. 13650–13659, 2024

    2024

  9. [17]

    Sastry, Nonlinear systems: Analysis, stability, and control

    S. Sastry, Nonlinear systems: Analysis, stability, and control. Springer, 1999

    1999

  10. [18]

    Zhou and J

    K. Zhou and J. C. Doyle, Essentials of robust control . Prentice Hall, 1998

    1998

  11. [19]

    G. E. Dullerud and F. Paganini, A course in robust control theory: A convex approach. Springer, 2001

    2001

  12. [20]

    Lozano, B

    R. Lozano, B. Brogliato, O. Egeland, and B. Maschke, Dissipative systems analysis and control: Theory and applications. Springer, 2013

    2013

  13. [21]

    System analysis via integral quadratic constraints,

    A. Megretski and A. Rantzer, “System analysis via integral quadratic constraints,” IEEE Trans. Autom. Control, vol. 42, no. 6, pp. 819–830, 1997

    1997

  14. [22]

    Stability analysis with dissipation inequalities and integral quadratic constraints,

    P. Seiler, “Stability analysis with dissipation inequalities and integral quadratic constraints,” IEEE Trans. Autom. Control , vol. 60, no. 6, pp. 1704–1709, 2014

    2014

  15. [23]

    Robust stability and performance analysis based on integral quadratic constraints,

    J. Veenman, C. W. Scherer, and H. K ¨oro˘glu, “Robust stability and performance analysis based on integral quadratic constraints,” Eur. J. Control, vol. 31, pp. 1–32, 2016

    2016

  16. [24]

    Non-parametric methods for L2-gain estimation using iterative experiments,

    B. Wahlberg, M. B. Syberg, and H. Hjalmarsson, “Non-parametric methods for L2-gain estimation using iterative experiments,” Auto- matica, vol. 46, no. 8, pp. 1376–1381, 2010

    2010

  17. [25]

    A trajectory-based framework for data- driven system analysis and control,

    J. Berberich and F. Allg ¨ower, “A trajectory-based framework for data- driven system analysis and control,” in Eur. Control Conf. (ECC) , pp. 1365–1370, IEEE, 2020

    2020

  18. [26]

    Provably robust verification of dissipativity properties from data,

    A. Koch, J. Berberich, and F. Allg ¨ower, “Provably robust verification of dissipativity properties from data,” IEEE Trans. Autom. Control , vol. 67, no. 8, pp. 4248–4255, 2021

    2021

  19. [27]

    Input-output data-driven control through dissipativity learning,

    W. Tang and P. Daoutidis, “Input-output data-driven control through dissipativity learning,” in Am. Control Conf. (ACC) , pp. 4217–4222, IEEE, 2019

    2019

  20. [28]

    Dissipativity learning control (DLC): A framework of input–output data-driven control,

    W. Tang and P. Daoutidis, “Dissipativity learning control (DLC): A framework of input–output data-driven control,” Comput. Chem. Eng., vol. 130, p. 106576, 2019

    2019

  21. [29]

    Dissipativity learning control (DLC): the- oretical foundations of input–output data-driven model-free control,

    W. Tang and P. Daoutidis, “Dissipativity learning control (DLC): the- oretical foundations of input–output data-driven model-free control,” Syst. Control Lett. , vol. 147, p. 104831, 2021

    2021

  22. [30]

    Issues with input-space representation in nonlinear data-based dissipativity estimation,

    E. LoCicero, A. Penne, and L. Bridgeman, “Issues with input-space representation in nonlinear data-based dissipativity estimation,” arXiv preprint, 2024. arXiv:2411.13404

    2024 arXiv

  23. [31]

    The stability of nonlinear dissipative systems,

    D. Hill and P. Moylan, “The stability of nonlinear dissipative systems,” IEEE Trans. Autom. Control , vol. 21, no. 5, pp. 708–711, 1976

    1976

  24. [32]

    IQC-synthesis with general dynamic multipliers,

    J. Veenman and C. W. Scherer, “IQC-synthesis with general dynamic multipliers,” Int. J. Robust Nonlin. Control , vol. 24, no. 17, pp. 3027– 3056, 2014

    2014

  25. [33]

    On orthonormal M ¨untz-Laguerre filters,

    L. Knockaert, “On orthonormal M ¨untz-Laguerre filters,” IEEE Trans. Signal Process., vol. 49, no. 4, pp. 790–793, 2001

    2001

  26. [34]

    Estimating the support of a high-dimensional distribu- tion,

    B. Sch ¨olkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, “Estimating the support of a high-dimensional distribu- tion,” Neur. Comput., vol. 13, no. 7, pp. 1443–1471, 2001

    2001

  27. [35]

    Feedback control of nonlinear differential-algebraic-equation systems,

    A. Kumar and P. Daoutidis, “Feedback control of nonlinear differential-algebraic-equation systems,” AIChE J. , vol. 41, no. 3, pp. 619–636, 1995

    1995

  28. [36]

    Data-driven control: Overview and per- spectives,

    W. Tang and P. Daoutidis, “Data-driven control: Overview and per- spectives,” in Am. Control Conf. (ACC) , pp. 1048–1064, IEEE, 2022

    2022

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.