pith. sign in

arxiv: 2606.04790 · v1 · pith:AREMUJ6Cnew · submitted 2026-06-03 · 📡 eess.SY · cs.SY· math.OC

A model-free approach to control barrier functions for higher-order systems

Pith reviewed 2026-06-28 05:17 UTC · model grok-4.3

classification 📡 eess.SY cs.SYmath.OC
keywords control barrier functionsfunnel controlmodel-free controlrelative degreesafety-critical systemsnonlinear controlrobotic manipulator
0
0 comments X

The pith

Control barrier functions can be designed model-free for nonlinear systems of arbitrary relative degree by using funnel control to identify safe inputs.

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

The paper tries to extend model-free control barrier functions, previously limited to relative-degree-one systems via prescribed performance or funnel methods, to systems of any relative degree. A direct carry-over of the relative-degree-one results does not work, so the authors develop new funnel-control characterizations that isolate a usable subset of controls satisfying the barrier condition. This is done without access to the system dynamics or full state measurements. The result matters because many physical systems, such as robotic arms, have relative degree greater than one and accurate models are often unavailable. Applicability is shown on a seven-degree-of-freedom manipulator.

Core claim

We extend the model-free CBF design to nonlinear systems of arbitrary relative degree. We utilize novel techniques from funnel control to characterize a subset of the controls satisfying a CBF condition without requiring a dynamic model or state measurement.

What carries the argument

Novel funnel-control techniques that isolate a non-empty subset of admissible controls meeting the CBF condition for relative degree greater than one.

If this is right

  • Safe controls become available for relative-degree-two systems such as robotic manipulators without dynamics knowledge.
  • The method applies to general nonlinear systems of any relative degree.
  • Modular safety filters can be realized in settings where only partial state information is available.
  • The approach is demonstrated to function on a seven-degree-of-freedom robot arm.

Where Pith is reading between the lines

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

  • The same funnel-based subset characterization might be combined with other performance-oriented controllers to obtain joint safety and transient guarantees.
  • Extension to output-feedback or observer-based settings would be a natural next step for partially observed plants.
  • Numerical checks on the size of the admissible control set for increasing relative degree would quantify practical conservatism.

Load-bearing premise

The funnel-control techniques can be combined with the CBF condition to produce a non-empty set of admissible controls for relative degree greater than one without a dynamic model or full state measurement.

What would settle it

Finding a concrete system of relative degree two for which the derived funnel-based condition admits no control input would falsify the claim.

Figures

Figures reproduced from arXiv: 2606.04790 by Dario Dennst\"adt, Johannes K\"ohler, Karl Worthmann, Lukas Lanza, Thomas Berger.

Figure 1
Figure 1. Figure 1: Evolution of the tracking error y − yref within the perfor￾mance boundary ψ. We consider the following conditions for the reference trajectory and the performance functions. Assumption 4. The reference trajectory yref belongs to the set Yref := Wr,∞(R≥0; R m). The performance function ψ is an element of the set Ψ :=  ψ ∈ W1,∞(R≥0; R) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Domains of the auxiliary variables β1, β2 from (12) for scalar ξ1, ξ2, yref ≡ 0, and ψ1 = ψ2 ≡ 1. The blank area −1 ≤ ξ1 ≤ 1 is a snapshot of D1 for a fixed time t, and the crosshatched area is a snapshot of D2, i.e., |ξ2 + ξ1 1−ξ 2 1 | < 1. We define the set D := {(t, ξ) ∈ Dr | kβr(t, ξ)k < 1} ⊂ Dr (13) and obtain the inclusion D ⊂ C for the safe set C from (5). Thus, ensuring that (t, y(t), y˙(t), . . . … view at source ↗
Figure 3
Figure 3. Figure 3: Initial and target configurations of the Kinova Gen3 ma￾nipulator used in simulations. the methods from [23] and [11] choose a desired velocity vd, e.g., based on the gradient of the constraint h, and then use a velocity controller to ensure an exponential decrease in the following Lyapunov-like function for the velocity error V (y, y, v ˙ d(t)) = ( ˙y − vd(t))⊤D(y)( ˙y − vd(t)). If the velocity controller… view at source ↗
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Control barrier functions (CBFs) are a widely applied modular tool to ensure safe operation of nonlinear dynamical control systems. However, for their construction accurate knowledge of the system dynamics is typically needed. This requirement was recently alleviated for relative-degree-one systems using techniques from prescribed performance control (PPC) or funnel control (FC). This article extends the model-free CBF design to nonlinear systems of arbitrary relative degree. Moreover, we show with a simple example that a straightforward extension of existing results for relative-degree-one systems fails. Instead, we utilize novel techniques from funnel control to characterize a subset of the controls satisfying a CBF condition without requiring a dynamic model or state measurement. Finally, we demonstrate the applicability of our results on a seven degrees of freedom robotic manipulator with relative degree two.

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 extends model-free CBF design from relative-degree-one systems to nonlinear systems of arbitrary relative degree. It first shows via a simple example that a direct extension of existing PPC/FC-based results fails, then uses novel funnel-control techniques to characterize a subset of admissible controls that satisfy a CBF condition from output measurements alone, without a dynamic model or full state. The approach is validated on a 7DOF robotic manipulator with relative degree two.

Significance. If the new characterization produces a non-empty admissible control set for relative degree >1 while remaining strictly model-free and output-based, the result would be a useful advance for safety-critical control of high-relative-degree systems (e.g., manipulators, vehicles) where accurate dynamics are unavailable. The explicit counter-example to the naive extension and the concrete robotic demonstration are concrete strengths.

major comments (2)
  1. [§4] §4 (characterization of admissible controls): the derivation that the funnel-control-based subset is non-empty for relative degree r>1 must be shown to avoid any implicit dependence on Lie derivatives of order greater than one or on unmeasured states; the current argument appears to rest on output-only measurements but the non-emptiness guarantee for the general class is not yet load-bearing.
  2. [§5] §5 (manipulator example): the specific performance functions, the exact form of the CBF inequality enforced, and the quantitative safety margins (e.g., minimum distance to the unsafe set) are not reported; without these it is impossible to verify that the model-free construction actually satisfies the higher-order CBF condition on the hardware.
minor comments (2)
  1. [§3] Notation for the funnel functions and the admissible-control set should be introduced with a single consistent symbol table to avoid confusion between the relative-degree-one and higher-order cases.
  2. [Abstract] The abstract states that the direct extension 'fails' but does not indicate the precise failure mode (empty set, loss of model-freeness, or violation of the CBF inequality); a one-sentence clarification would help readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the positive assessment of the manuscript's contributions. We address each major comment below and will incorporate clarifications and additional details in the revised version.

read point-by-point responses
  1. Referee: [§4] §4 (characterization of admissible controls): the derivation that the funnel-control-based subset is non-empty for relative degree r>1 must be shown to avoid any implicit dependence on Lie derivatives of order greater than one or on unmeasured states; the current argument appears to rest on output-only measurements but the non-emptiness guarantee for the general class is not yet load-bearing.

    Authors: We appreciate this point. The derivation in §4 constructs the admissible control subset exclusively via the output-based funnel error transformation and the associated performance functions; no Lie derivatives of order greater than one or unmeasured states enter the argument. Non-emptiness follows directly from the existence of a funnel controller for relative-degree-r systems that keeps the transformed error within the prescribed bounds, thereby satisfying the CBF condition by construction. To address the concern that this guarantee is not yet sufficiently load-bearing, we will add an explicit remark and a short appendix paragraph restating the output-only nature of the construction and confirming the absence of higher-order derivatives. revision: partial

  2. Referee: [§5] §5 (manipulator example): the specific performance functions, the exact form of the CBF inequality enforced, and the quantitative safety margins (e.g., minimum distance to the unsafe set) are not reported; without these it is impossible to verify that the model-free construction actually satisfies the higher-order CBF condition on the hardware.

    Authors: We agree that these implementation details are required for full verification. In the revised manuscript we will report the exact performance functions employed for the 7DOF manipulator, the precise CBF inequality that is enforced at each step, and quantitative safety margins including the minimum observed distance to the unsafe set throughout the hardware experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: model-free CBF extension for higher relative degree relies on external funnel-control techniques

full rationale

The paper's central step is to characterize a non-empty admissible control set for relative-degree >1 systems using novel funnel-control techniques, without model or full state. The abstract explicitly states that a direct extension of the rel-degree-1 case fails and therefore invokes external funnel-control methods to produce the characterization. No equation or claim reduces by construction to a fitted parameter, self-defined quantity, or self-citation chain; the load-bearing novelty is presented as an independent combination of established PPC/FC results with the CBF inequality. The derivation is therefore self-contained against external benchmarks in the funnel-control literature.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim rests on the existence of a non-empty admissible control set derived from funnel-control performance bounds; the abstract does not introduce new free parameters, axioms, or invented entities beyond the standard CBF and funnel-control machinery.

pith-pipeline@v0.9.1-grok · 5673 in / 1194 out tokens · 15702 ms · 2026-06-28T05:17:11.219639+00:00 · methodology

discussion (0)

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

Reference graph

Works this paper leans on

36 extracted references · 2 canonical work pages · 1 internal anchor

  1. [1]

    Alan, A., Taylor, A.J., He, C.R., Ames, A.D., Orosz, G., 2023. Control barrier functions and input-to-state safety with appli- 8 Time [s] 0 1 2 3 4 5 Joint Angles [rad] -4 -3 -2 -1 0 1 2 3 q1 q2 q3 q4 q5 q6 q7 Time [s] 0 1 2 3 4 5 Joint velocities [rad/s] -1.5 -1 -0.5 0 0.5 1 1.5 v1 v2 v3 v4 v5 v6 v7 Time [s] 0 1 2 3 4 5 Torque [Nm] -15 -10 -5 0 5 10 15 2...

  2. [2]

    Control barrier functions: Theory and applications, in: Proc

    Ames, A.D., Coogan, S., Egerstedt, M., Notomista, G., Sreenath, K., Tabuada, P., 2019. Control barrier functions: Theory and applications, in: Proc. 18th European Control Conf. (ECC), pp. 3420–3431

  3. [3]

    Control barrier function based quadratic programs for safety critical sys- tems

    Ames, A.D., Xu, X., Grizzle, J.W., Tabuada, P., 2016. Control barrier function based quadratic programs for safety critical sys- tems. IEEE Transactions on Automatic Control 62, 3861–3876

  4. [4]

    Robust adaptive con- trol of feedback linearizable MIMO nonlinear systems with pre- scribed performance

    Bechlioulis, C.P., Rovithakis, G.A., 2008. Robust adaptive con- trol of feedback linearizable MIMO nonlinear systems with pre- scribed performance. IEEE Transactions on Automatic Control 53, 2090–2099

  5. [5]

    A low-complexity global approximation-free control scheme with prescribed per- formance for unknown pure feedback systems

    Bechlioulis, C.P., Rovithakis, G.A., 2014. A low-complexity global approximation-free control scheme with prescribed per- formance for unknown pure feedback systems. Automatica 50, 1217–1226

  6. [6]

    Funnel control of nonlinear systems

    Berger, T., Ilchmann, A., Ryan, E.P., 2021. Funnel control of nonlinear systems. Math. Control Signals Syst. 33, 151–194

  7. [7]

    Funnel control for nonlinear systems with known strict relative degree

    Berger, T., Lê, H.H., Reis, T., 2018. Funnel control for nonlinear systems with known strict relative degree. Automatica 87, 345– 357

  8. [8]

    Safe learning in robotics: From learning-based control to safe reinforcement learning

    Brunke, L., Greeff, M., Hall, A.W., Yuan, Z., Zhou, S., Panerati, J., Schoellig, A.P., 2022. Safe learning in robotics: From learning-based control to safe reinforcement learning. Annual Review of Control, Robotics, and Autonomous Systems 5, 411– 444

  9. [9]

    Robust control barrier functions with sector-bounded uncertainties

    Buch, J., Liao, S.C., Seiler, P., 2021. Robust control barrier functions with sector-bounded uncertainties. IEEE Control Sys- tems Letters 6, 1994–1999

  10. [10]

    Asymptotic stabilization of min- imum phase nonlinear systems

    Byrnes, C.I., Isidori, A., 1991. Asymptotic stabilization of min- imum phase nonlinear systems. IEEE Transactions on Auto- matic Control 36, 1122–1137

  11. [11]

    Safety-critical control for autonomous systems: Control barrier functions via reduced-order models

    Cohen, M.H., Molnar, T.G., Ames, A.D., 2024. Safety-critical control for autonomous systems: Control barrier functions via reduced-order models. Annual Reviews in Control 57, 100947

  12. [12]

    Robot manipulator capability in matlab: A tutorial on using the robotics system toolbox [tutorial]

    Corke, P., 2017. Robot manipulator capability in matlab: A tutorial on using the robotics system toolbox [tutorial]. IEEE Robotics & Automation Magazine 24, 165–166

  13. [13]

    Dean, S., Taylor, A., Cosner, R., Recht, B., Ames, A.,

  14. [14]

    Con- ference on Robot Learning, PMLR

    Guaranteeing safety of learned perception modules via measurement-robust control barrier functions, in: Proc. Con- ference on Robot Learning, PMLR. pp. 654–670

  15. [15]

    Dhiman, V., Khojasteh, M.J., Franceschetti, M., Atanasov, N.,

  16. [16]

    IEEE Transactions on Automatic Control 68, 214–229

    Control barriers in bayesian learning of system dynamics. IEEE Transactions on Automatic Control 68, 214–229

  17. [17]

    Towards a framework for realizable safety crit- ical control through active set invariance, in: Proc

    Gurriet, T., Singletary, A., Reher, J., Ciarletta, L., Feron, E., Ames, A., 2018. Towards a framework for realizable safety crit- ical control through active set invariance, in: Proc. ACM/IEEE 9th International Conference on Cyber-Physical Systems (IC- CPS), IEEE. pp. 98–106

  18. [18]

    From PID to active disturbance rejection control

    Han, J., 2009. From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics 56, 900–906

  19. [19]

    Tracking with prescribed transient behaviour

    Ilchmann, A., Ryan, E.P., Sangwin, C.J., 2002. Tracking with prescribed transient behaviour. ESAIM: Control, Optimisation and Calculus of Variations 7, 471–493

  20. [20]

    Robust control barrier functions for con- strained stabilization of nonlinear systems

    Jankovic, M., 2018. Robust control barrier functions for con- strained stabilization of nonlinear systems. Automatica 96, 359– 367

  21. [21]

    Input-to-state safety with control barrier functions

    Kolathaya, S., Ames, A.D., 2018. Input-to-state safety with control barrier functions. IEEE Control Systems Letters 3, 108– 113

  22. [22]

    Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinear systems with prescribed performance

    Kostarigka, A.K., Rovithakis, G.A., 2011. Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinear systems with prescribed performance. IEEE Transac- tions on Neural Networks and Learning Systems 23, 138–149

  23. [23]

    Sampled-data funnel con- trol and its use for safe continual learning

    Lanza, L., Dennstädt, D., Worthmann, K., Schmitz, P., Şen, G.D., Trenn, S., Schaller, M., 2024. Sampled-data funnel con- trol and its use for safe continual learning. Systems & Control Letters 192, 105892

  24. [24]

    A model-free approach to control barrier functions using funnel control

    Lanza, L., Köhler, J., Dennstädt, D., Berger, T., Worthmann, K., 2025. A model-free approach to control barrier functions using funnel control. IEEE Control Systems Letters 9, 1183–

  25. [25]

    doi: 10.1109/LCSYS.2025.3581519

  26. [26]

    Model-free safety-critical control for robotic systems

    Molnar, T.G., Cosner, R.K., Singletary, A.W., Ubellacker, W., Ames, A.D., 2021. Model-free safety-critical control for robotic systems. IEEE Robotics and Automation Letters 7, 944–951

  27. [27]

    On the equivalence between prescribed performance control and control barrier functions, in: Proc

    Namerikawa, R., Wiltz, A., Mehdifar, F., Namerikawa, T., Di- marogonas, D.V., 2024. On the equivalence between prescribed performance control and control barrier functions, in: Proc. American Control Conference (ACC), pp. 2458–2463

  28. [28]

    Partial feedback linearization of underac- tuated mechanical systems, in: Proc

    Spong, M.W., 1994. Partial feedback linearization of underac- tuated mechanical systems, in: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 94), pp. 314–321

  29. [29]

    Reinforcement learning: An introduction

    Sutton, R.S., Barto, A.G., et al., 1998. Reinforcement learning: An introduction. volume 1. MIT press Cambridge

  30. [30]

    Learning for safety-critical control with control barrier functions

    Taylor, A., Singletary, A., Yue, Y., Ames, A., 2020. Learning for safety-critical control with control barrier functions. PMLR (2nd Conf. Learning for Dynamics and Control) 120, 708–717

  31. [31]

    Funnel control for uncertain nonlinear systems via zeroing control barrier functions

    Verginis, C.K., 2022. Funnel control for uncertain nonlinear systems via zeroing control barrier functions. IEEE Control Systems Letters 7, 853–858

  32. [32]

    Data-driven safety filters: Hamilton-Jacobi reachability, control barrier functions, and predictive methods for uncertain systems

    Wabersich, K.P., Taylor, A.J., Choi, J.J., Sreenath, K., Tomlin, C.J., Ames, A.D., Zeilinger, M.N., 2023. Data-driven safety filters: Hamilton-Jacobi reachability, control barrier functions, and predictive methods for uncertain systems. IEEE Control 9 Systems Magazine 43, 137–177

  33. [33]

    Constructive safety using con- trol barrier functions

    Wieland, P., Allgöwer, F., 2007. Constructive safety using con- trol barrier functions. IF AC Proceedings Volumes 40, 462–467

  34. [34]

    A neural signed configuration distance function for path planning of picking manipulators

    Wullt, B., Norrlöf, M., Mattsson, P., Schön, T.B., 2025. Prob- abilistic bubble roadmap. arXiv preprint arXiv:2502.16205

  35. [35]

    High-order control barrier functions

    Xiao, W., Belta, C., 2021. High-order control barrier functions. IEEE Transactions on Automatic Control 67, 3655–3662

  36. [36]

    Constrained control of input–output linearizable systems using control sharing barrier functions

    Xu, X., 2018. Constrained control of input–output linearizable systems using control sharing barrier functions. Automatica 87, 195–201. Appendix Proof of Lemma 13 . We adapt the proof of [ 21, Lem. 2.2] to the current setting. For i 2 [r 1], we define σi := inf s≥ˆt k ψi+1(s) ψi(s) k. Setting δ0 := 0, ¯κ0 := 0 and uti- lizing the the short hand notation χ(...