pith. sign in

arxiv: 2605.21344 · v1 · pith:MOLROQYEnew · submitted 2026-05-20 · 🧮 math.OC · cs.SY· eess.SY

Beyond Nonlinear Small-Gain Design: DADS with Partial-State Feedback

Pith reviewed 2026-05-21 03:11 UTC · model grok-4.3

classification 🧮 math.OC cs.SYeess.SY
keywords DADS controlpartial-state feedbackPDE-ODE interconnectionrobust regulationinput-to-output stabilitysmall-gain theoreminfinite-dimensional systems
0
0 comments X

The pith

A partial-state DADS controller achieves robust regulation for an ODE interconnected with unknown PDEs without small-gain conditions or bound knowledge.

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

This paper develops a Deadzone-Adapted Disturbance Suppression controller that uses only partial-state feedback to regulate a scalar ODE connected to an unknown infinite-dimensional system. By relying on the input-to-output stability properties of the PDE subsystem, the design avoids the usual small-gain theorem requirements. The approach proves robust regulation to zero in the presence of disturbances for three distinct cases: heat equation, transport equation, and wave equation with viscous damping. The same controller structure works across all cases without any prior knowledge of disturbance or parameter bounds. This matters because it offers a simpler way to stabilize complex coupled systems where full models and bounds are unavailable.

Core claim

The central claim is that the DADS control law with partial-state feedback ensures asymptotic regulation of the ODE state for the interconnected system, leveraging the output asymptotic gain property of the PDE part, and this holds uniformly for the heat, transport, and damped wave PDEs without requiring bounds on the disturbances or system parameters.

What carries the argument

The Deadzone-Adapted Disturbance Suppression (DADS) controller, which adapts to disturbances using a deadzone mechanism based on the output of the ODE subsystem.

If this is right

  • The controller achieves regulation without assuming bounds on disturbances or parameters.
  • The same controller design applies to heat, transport, and wave PDE interconnections.
  • Robust regulation holds even with external inputs to the system.
  • Small-gain conditions are bypassed due to the IOS property of the PDE.
  • Partial-state feedback suffices for the regulation task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could extend to other types of infinite-dimensional systems if they satisfy similar stability properties.
  • Implementation might allow control of physical systems like thermal or fluid processes with limited sensors.
  • Future work could test the controller on experimental setups involving PDE dynamics.

Load-bearing premise

The PDE subsystem must satisfy the input-to-output stability or output asymptotic gain property with respect to the ODE input.

What would settle it

A counterexample where the interconnected system violates the output asymptotic gain property and the ODE state fails to converge to zero under the proposed DADS controller would disprove the claim.

read the original abstract

Eduardo Sontag and coauthors studied Input-to-Output Stability (IOS) and the output asymptotic gain property. These notions changed control theory and recently had an impact on robust adaptive control through the Deadzone-Adapted Disturbance Suppression (DADS) control scheme. Moreover, recently the notion of IOS was extended to systems described by Partial Differential Equations (PDEs). In this work, we celebrate Eduardo Sontag by combining DADS and IOS for PDEs: we study the partial-state regulation problem for a scalar Ordinary Differential Equation (ODE) which is interconnected with a possibly infinite-dimensional system. In such a case the DADS control scheme can allow an escape from the requirements of the small-gain theorem that is mainly used for partial-state feedback. We show the design procedure of partial-state DADS controllers and we prove robust regulation even in the presence of external inputs (disturbances) without assuming knowledge of any disturbance/parameter bounds. The DADS controller is applied to three different cases of the interconnection of an ODE with an almost completely unknown: (a) heat PDE, (b) transport PDE, and (c) wave PDE with viscous damping. We show that the same DADS controller can achieve robust regulation in all three cases.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes extending the Deadzone-Adapted Disturbance Suppression (DADS) scheme, originally based on Input-to-Output Stability (IOS) and output asymptotic gain properties, to the partial-state regulation of a scalar ODE interconnected with an infinite-dimensional system. It claims that a single fixed DADS controller, designed from the ODE subsystem, achieves robust regulation for the ODE connected to any of three PDEs (heat, transport, or damped wave) without requiring knowledge of disturbance or parameter bounds, thereby bypassing the small-gain theorem.

Significance. If the central claims hold with the stated generality, the work would provide a unified, bound-free adaptive controller for ODE-PDE cascades that leverages recent PDE extensions of IOS. This could reduce conservatism in robust control designs for distributed systems and offer a concrete alternative to small-gain methods when only partial state is available.

major comments (2)
  1. [PDE application subsections (heat, transport, damped wave)] The claim that the identical DADS controller works uniformly for arbitrary positive PDE coefficients (diffusion constant, transport speed, damping coefficient) rests on the interconnected system satisfying the output asymptotic gain property independently of those coefficients. The applications sections for the three PDE cases must explicitly show that the gain function derived from the PDE interconnection does not depend on the unknown coefficients or that the DADS deadzone adaptation dominates any possible gain; otherwise the 'almost completely unknown' and 'same controller' statements do not follow.
  2. [Main stability theorem and its proof] The proof that DADS escapes small-gain requirements via IOS for the partial-state feedback case needs to verify that the adaptation law remains well-defined and the closed-loop trajectories remain bounded when the PDE parameters vary over all positive reals, without implicit bounds introduced through the specific PDE analysis (maximum principle, characteristics, or energy dissipation).
minor comments (2)
  1. [Controller design section] Define the deadzone function and its width parameter explicitly at first use, and clarify how the adaptation gain is chosen relative to the IOS gain function.
  2. [Problem formulation] Add a short remark on the precise sense in which the PDE state is 'almost completely unknown' (e.g., whether initial conditions or boundary conditions are also arbitrary).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thorough review and insightful comments on our manuscript. We address each major comment point by point below, providing clarifications based on the existing analysis while agreeing to strengthen the presentation where needed for greater explicitness.

read point-by-point responses
  1. Referee: [PDE application subsections (heat, transport, damped wave)] The claim that the identical DADS controller works uniformly for arbitrary positive PDE coefficients (diffusion constant, transport speed, damping coefficient) rests on the interconnected system satisfying the output asymptotic gain property independently of those coefficients. The applications sections for the three PDE cases must explicitly show that the gain function derived from the PDE interconnection does not depend on the unknown coefficients or that the DADS deadzone adaptation dominates any possible gain; otherwise the 'almost completely unknown' and 'same controller' statements do not follow.

    Authors: We appreciate the referee's emphasis on making the parameter independence fully transparent. In the heat PDE subsection, the output asymptotic gain is derived via the maximum principle applied to the parabolic equation, yielding a gain that depends only on the interconnection structure and the ODE output, not on the diffusion constant (which affects transient decay but not the steady-state gain bound). For the transport PDE, the method of characteristics produces an explicit gain independent of the transport speed. For the damped wave, energy methods give a gain uniform in the damping coefficient. The DADS deadzone adaptation is constructed precisely to dominate any such finite gain without knowledge of its value or the underlying coefficients. To strengthen the manuscript, we will insert explicit remarks in each applications subsection stating the parameter independence of the derived gains and confirming that the same DADS controller applies uniformly. revision: yes

  2. Referee: [Main stability theorem and its proof] The proof that DADS escapes small-gain requirements via IOS for the partial-state feedback case needs to verify that the adaptation law remains well-defined and the closed-loop trajectories remain bounded when the PDE parameters vary over all positive reals, without implicit bounds introduced through the specific PDE analysis (maximum principle, characteristics, or energy dissipation).

    Authors: The main stability result (Theorem 3.1) is proved using the IOS property of the PDE subsystem together with the DADS adaptation law, which depends solely on the measured ODE state and does not require PDE parameters. Well-definedness of the adaptation follows directly from the deadzone function being Lipschitz and the closed-loop ODE being locally Lipschitz. Boundedness is obtained from the definition of output asymptotic gain combined with the DADS mechanism that prevents the effective input from growing unbounded, without invoking a small-gain condition. The PDE-specific analyses (maximum principle, characteristics, energy) are used only to establish that an IOS property holds with some finite gain; they do not introduce hidden parameter bounds into the overall argument because the gain estimates remain finite and uniform for all positive coefficients. Nevertheless, to eliminate any possible ambiguity, we will add a clarifying paragraph in the proof of Theorem 3.1 noting the uniformity over positive PDE parameters. revision: partial

Circularity Check

0 steps flagged

No circularity: DADS-IOS application to ODE-PDE systems uses independent case analysis

full rationale

The paper's derivation proceeds by invoking the established IOS and output asymptotic gain properties (from Sontag et al.) as extended to PDEs, then designing a partial-state DADS controller for the ODE and verifying robust regulation for each of the three specific PDE interconnections (heat, transport, damped wave) via their individual structural properties. No step reduces by construction to a fitted parameter, self-definition, or unverified self-citation chain; the same controller succeeding across cases follows from the per-PDE proofs rather than from renaming or smuggling an ansatz. The absence of knowledge of bounds is a consequence of the IOS framework applied to each interconnection, not an input that is redefined as output. This is a standard non-circular theoretical construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review performed on abstract only; full text unavailable so free parameters, axioms, and invented entities cannot be enumerated from the manuscript.

pith-pipeline@v0.9.0 · 5759 in / 1080 out tokens · 25439 ms · 2026-05-21T03:11:58.192261+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

37 extracted references · 37 canonical work pages

  1. [1]

    Supervision of Integral -Input-to-State Stabilizing Controllers

    Hespanha, J. P., D. Liberzon and A. S. Morse, “Supervision of Integral -Input-to-State Stabilizing Controllers”, Automatica, 38, 2002, 1327-1335

  2. [2]

    Ioannou, P. A. and J. Sun, Robust Adaptive Control, Englewood Cliffs, NJ, USA: Prentice Hall, 1996

  3. [3]

    Ioannou, P. A. and B. Fidan, Adaptive Control Tutorial, SIAM, 2006

  4. [4]

    Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties

    Jiang, Z. -P. and L. Praly, “Design of Robust Adaptive Controllers for Nonlinear Systems with Dynamic Uncertainties”, Automatica, 34, 1998, 825-840

  5. [5]

    Small -Gain Theorem for ISS Systems a nd Applications

    Jiang, Z. -P., A. Teel and L. Praly, “Small -Gain Theorem for ISS Systems a nd Applications”, Mathematics of Control, Signals, and Systems, 7, 1994, 95-120

  6. [6]

    Necessary and Sufficient Lyapunov-like Conditions for Robust Nonlinear Stabilization

    Karafyllis, I. and Z.-P. Jiang, “ Necessary and Sufficient Lyapunov-like Conditions for Robust Nonlinear Stabilization ”, ESAIM Control, Optimisation and Calculus of Variations , 16, 2010, 887-928

  7. [7]

    and Z.-P

    Karafyllis, I. and Z.-P. Jiang, Stability and Stabilization of Nonlinear Systems , Springer-Verlag, London (Series: Communications and Control Engineering), 2011

  8. [8]

    Karafyllis, I. and M. Krstic, Input-to-State Stability for PDEs, Springer-Verlag, London (Series: Communications and Control Engineering), 2019

  9. [9]

    Boundary-to-Displacement Asymptotic Gains for Wave Systems With Kelvin-Voigt Damping

    Karafyllis, I., M. Kontorinaki and M. Krstic, “Boundary-to-Displacement Asymptotic Gains for Wave Systems With Kelvin-Voigt Damping”, International Journal of Control , 94, 2021, 2822- 2833

  10. [10]

    Lyapunov Conditions for Uniform Asymptotic Output Stability and a Relaxation of Barbălat’s Lemma

    Karafyllis, I. and A. Chaillet, “Lyapunov Conditions for Uniform Asymptotic Output Stability and a Relaxation of Barbălat’s Lemma”, Automatica, 132, 2021, 109792

  11. [11]

    ISS-Based Robustness to Various Neglecte d Damping Mechanisms for the 1 -D Wave PDE

    Karafyllis, I. and M. Krstic , “ ISS-Based Robustness to Various Neglecte d Damping Mechanisms for the 1 -D Wave PDE ”, Mathematics of Control, Signals, and Systems , 35, 2023, 741–779

  12. [12]

    Adaptive Regulation with Global KL Guarantees

    Karafyllis, I., A. Aslanidis and M. Krstic, “ Adaptive Regulation with Global KL Guarantees ”, IEEE Transactions on Automatic Control, 69, 2024, 2-15

  13. [13]

    Deadzone-Adapted Disturbance Suppression Control for Global Practical IOS and Zero Asymptotic Gain to Matched Uncertainties

    Karafyllis, I. and M. Krstic, “ Deadzone-Adapted Disturbance Suppression Control for Global Practical IOS and Zero Asymptotic Gain to Matched Uncertainties”, Systems & Control Letters , 185, 2024, 105746

  14. [14]

    Deadzone-Adapted Disturbance Suppression Control for Strict-Feedback Systems

    Karafyllis, I., M. Krstic and A. As lanidis, “ Deadzone-Adapted Disturbance Suppression Control for Strict-Feedback Systems”, Automatica, 171, 2025, 111986

  15. [15]

    Karafyllis, I. and M. Krstic, Robust Adaptive Control: Deadzone -Adapted Disturbance Suppression, SIAM, 2025

  16. [16]

    Partial-State DADS Control for Matched Unmodeled Dynamics

    Karafyllis, I. and M. Krstic, “ Partial-State DADS Control for Matched Unmodeled Dynamics”, Automatica, 188, 2026, 112930. 30

  17. [17]

    DADS Under Unknown Input Coefficients

    Karafyllis, I. and M. Krstic, “DADS Under Unknown Input Coefficients”, to appear in the International Journal of Control (see also arXiv:2510.04117 [math.OC])

  18. [18]

    K., Nonlinear Systems, 2nd Edition, Prentice-Hall, 1996

    Khalil, H. K., Nonlinear Systems, 2nd Edition, Prentice-Hall, 1996

  19. [19]

    Dynamic High -Gain Scaling: State and Output Feedback With Application to Systems With ISS Appended Dynamics Driven by All States

    Krishnamurthy, P. and F. Khorrami, “Dynamic High -Gain Scaling: State and Output Feedback With Application to Systems With ISS Appended Dynamics Driven by All States”, IEEE Transactions on Automatic Control, 49, 2004, 2219-2239

  20. [20]

    Kanellakopoulos and P

    Krstic, M., I . Kanellakopoulos and P. Kokotovic, Nonlinear and Adaptive Control Design , Wiley, 1995

  21. [21]

    Modular Approach to Adaptive Nonlinear Stabilization

    Krstic, M. and P. Kokotovic, “ Modular Approach to Adaptive Nonlinear Stabilization”, Automatica, 32, 1996, 625-629

  22. [22]

    Input-to-State Stability of a Clamped -Free Damped String in the Presence of Distributed and Boundary Disturbances

    Lhachemi, H., D. Saussie, G. Zhu and R. Sh orten, “Input-to-State Stability of a Clamped -Free Damped String in the Presence of Distributed and Boundary Disturbances”, IEEE Transactions on Automatic Control, 65, 2020, 1248-1255

  23. [23]

    Universal Adaptive Control of Nonlinear Systems

    Lopez, B. T. and J. -J. E. Slotine, “Universal Adaptive Control of Nonlinear Systems”, IEEE Control Systems Letters, 6, 2022, 1826-1830

  24. [24]

    Robust Stabilization of Feedback Linearizable Time -Varying Uncertain Nonlinear Systems

    Marino, R. and P. Tomei, “Robust Stabilization of Feedback Linearizable Time -Varying Uncertain Nonlinear Systems”, Automatica, 29, 1993, 181-189

  25. [25]

    Theory and Applications, Springer, 2023

    Mironchenko, A., Input-to-State Stability. Theory and Applications, Springer, 2023

  26. [26]

    Input-to-State Stability of Infinite -Dimensional Systems: Recent Results and Open Questions

    Mironchenko, A. and C. Prieur, “ Input-to-State Stability of Infinite -Dimensional Systems: Recent Results and Open Questions”, SIAM Review, 62, 2020, 529-614

  27. [27]

    Narendra, K. S. and A. M. Annaswamy, Stable Adaptive Systems, Dover, 2005

  28. [28]

    Bounded Error Adaptive Control

    Peterson, B. B. and K. S. Narendra, “Bounded Error Adaptive Control”, IEEE Transactions on Automatic Control, 27, 1982, 1161-1168

  29. [29]

    Linear Output Feedback with Dynamic High Gain for Nonlinear Systems

    Praly, L. and Z. -P. Jia ng, “Linear Output Feedback with Dynamic High Gain for Nonlinear Systems”, Systems & Control Letters, 53, 2004, 107-116

  30. [30]

    Sastry, S. and M. Bodson, Adaptive Control: Stability, Convergence, and Robustness , Dover, 2011

  31. [31]

    Smooth Stabi lization Implies Coprime Factorization

    Sontag, E. D., “Smooth Stabi lization Implies Coprime Factorization”, IEEE Transactions on Automatic Control, 34, 1989, 435-443

  32. [32]

    On Characterizations of the Input -to-State Stability Property

    Sontag, E. D. and Y. Wang, “On Characterizations of the Input -to-State Stability Property”, Systems & Control Letters, 24, 1995, 351-359

  33. [33]

    New Characterizations of the Input -to-State Stability

    Sontag, E. D. and Y. Wang, “New Characterizations of the Input -to-State Stability”, IEEE Transactions on Automatic Control, 41, 1996, 1283-1294

  34. [34]

    Comments on Integral Variants of ISS

    Sontag, E. D., “Comments on Integral Variants of ISS”, Systems & Control Letters, 34, 1998, 93-100

  35. [35]

    Notions of Input to Output Stability

    Sontag, E. D. and Y. Wang, “Notions of Input to Output Stability”, Systems & Control Letters, 38, 1999, 235-248

  36. [36]

    Lyapunov Characterizations of Input -to-Output Stability

    Sontag, E. D. and Y. Wang, “Lyapunov Characterizations of Input -to-Output Stability”, SIAM Journal on Control and Optimization, 39, 2001, 226-249

  37. [37]

    Input -to-State Stability: Basic Concepts and Results

    Sontag, E. D., “Input -to-State Stability: Basic Concepts and Results”, in P. Nistri and G. Stefani (eds) Nonlinear and Optimal Control Theory. Lectures Given at the C.I.M.E. Summer School Held in Cetraro, Italy, June 19 -29, 2004, vol. 1932, Lecture No tes in Mathematics, 163 - 220, Springer, Berlin, 2008