Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots

Adri\`a Momp\'o Alepuz; Dimitrios Papageorgiou; Silvia Tolu

REVIEW 4 major objections 5 minor 32 references

Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Learned compensation for a nonlinear Smith predictor reduces the XY tracking error of a two-module soft robot arm by up to 64% at high controller gains, allowing stable high-gain motion despite the arm's 0.14-second delay.

desk verdict Real experimental improvement, but a missing linear-predictor baseline undercuts the claimed nonlinear contribution. read the letter →

arxiv 2504.12428 v1 pith:44OJNOO2 submitted 2025-04-16 cs.RO cs.SYeess.SY

Adrià Mompó Alepuz , Dimitrios Papageorgiou , Silvia Tolu This is my paper · ORCID

classification cs.ROcs.SYeess.SY

keywords softrobotcontrolSmithpredictordelaycompensationKernelRecursiveLeastSquaresLegendreNetworkslidingmodeonlinelearningassistiverobotics

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 tries to establish that a learning-based approximation of a nonlinear Smith predictor can compensate the fixed 0.14-second input-output delay of a two-module soft robot arm, and that doing so lets the existing super-twisting sliding-mode controller run at higher gains without the oscillations that normally appear. The method trains a Kernel Recursive Least Squares Tracker online to reproduce the Smith predictor's integral term, using a Legendre Delay Network to compress the recent command history into three state variables. On a circular XY tracking task, the learned predictor reduced stable-phase RMS tracking error from 27.6 mm to 9.9 mm at high controller gains (a 64% reduction) and from 14.4 mm to 8.3 mm at medium gains, with the differences statistically significant at p < 0.0001. If correct, the approach is a way to get accurate, stable tracking from soft arms without an accurate analytical model of their nonlinear dynamics.

What carries the argument

The load-bearing object is the learning predictor block: a KRLST regressor that takes as input the 6D pose, the observer-estimated 6D velocity, and either a 3-dimensional LDN memory state $m(t) \in \mathbb{R}^3$ or the raw actuator history $u[t-d,t]$ (3 or 7 steps), and outputs $\hat{y}(t)$. Training uses shifted data: at time $t$ the input is $\{X(t-d), u[t-2d,t-d]\}$ and the target is the measured pose difference $x(t) - x(t-d)$, so the regressor learns the delayed state change that the Smith predictor integral would compute. The LDN is the linear system $\theta \dot{m}(t) = A m(t) + B u(t)$ with memory length $\theta = d = 0.14$ s and $p = 3$, derived from a Pad\'e approximation of the delay, which compresses seven samples of control history into three states. KRLST supplies online adaptation, a bounded dictionary of size 80, and a forgetting factor, so the learned compensation tracks changing robot dynamics.

What would settle it

Keep the controller and gains fixed but add an extra, unaccounted communication latency (say 0.05 s) or hang a payload that changes the arm's effective delay; if the learned predictor's modeling error reverts to the No-Pred level and tracking error returns to the baseline, the delay-compensation claim is specific to the fixed 0.14 s delay. A cleaner direct test is to run the identical KRLST/LDN predictor on a simulated nonlinear system with known delay, where the true Smith integral $\Delta x_p(t)$ can be computed exactly, and check whether $\hat{y}(t)$ matches it rather than merely matching $x(t)-x(t-d)$.

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Extended reading notes

Core claim

The central claim is that the integral term $\Delta x_p(t) = \int_{t-d}^{t} F_x(X_p(\tau), u(\tau))\,d\tau$ of a nonlinear Smith predictor, which is the predicted pose change over the delay, can be approximated online by a learned mapping $\hat{y}(t) \approx \Delta x_p(t)$ whose inputs are the current complete state $X(t) = [x(t), \dot{\hat{x}}(t)]$ and a compressed history of recent control actions, and that substituting $x_p(t) = x(t) + \hat{y}(t)$ into the sliding-mode error stabilizes high-gain tracking. The authors argue this is justified because the integral depends only on the control history $u[t-d,t]$ and the predicted state at time $t-d$, which is approximated by the current measured state. They report that all learning variants significantly outperform the baseline controller in the stable phase at medium and high gains, with the LDN-compressed variant statistically better at modeling the delay-period state change, and that both the 3-state compressed history and the full 7-step history give comparable tracking performance.

Load-bearing premise

The approach assumes the input-output delay is a fixed, known 0.14 seconds and that the measured pose difference $x(t)-x(t-d)$ is a faithful training target for the Smith predictor integral; if the delay varies with load or the measured state difference is corrupted by disturbances, the learned predictor compensates the wrong quantity.

Editorial extensions

If this is right

At medium and high controller gains, the learning-based methods lowered stable-phase XY RMS error from 14.4 mm to 8.3 mm and from 27.6 mm to 9.9 mm, respectively, reductions of 42% and 64% relative to the sliding-mode baseline.
The LDN-compressed variant matched the raw 7-step history in stable tracking while using only 3 memory states, and was statistically better at modeling the delayed state change (2.35 vs 2.55 mm RMS at medium gain).
Because KRLST adapts online with a forgetting mechanism, the compensation does not require an accurate analytical model of the soft arm's nonlinear dynamics.
At low gains all methods perform similarly, so the demonstrated benefit is specifically the ability to raise controller gains without the oscillation that degrades the baseline.
The authors report qualitatively smoother motion in preliminary human-guided trajectories, pointing toward assistive tasks where reference trajectories are not fixed in advance.

Reading between the lines

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

Beyond the paper: because the training target is the measured closed-loop state difference, the learned predictor may partly absorb whatever controller was active during data collection, so the reported gains might not transfer to new gains, trajectories, or users without retraining.
Beyond the paper: the closeness of Hist-3, Hist-7, and LDN-3 suggests the 0.14 s delay is short enough that the most recent three control steps carry most of the information, so the compression advantage would likely show up mainly for longer or variable delays.
Beyond the paper: with a fixed known delay of 0.14 s at 50 Hz, a simple tapped delay line could also supply the history; the learning block's real contribution may be absorbing model mismatch rather than compressing memory, a separation the current ablation cannot make.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a competent experimental paper that shows a meaningful reduction in tracking error on a two-module soft arm, but the central attribution—that the KRLST/LDN learned nonlinear Smith predictor is what gets you the gain—is not fully supported because there's no comparison against the trivial linear predictor x + d·ẋ̂. That's the one thing worth pushing on.

What's genuinely here: the combination of LDN for input history compression and KRLST for online learning is clean, and the experiments are properly done. Twenty runs, ANOVA/Tukey, consistent error reductions across medium and high gains. The improvement at high gains (27.6 to ~9.9 mm) is substantial, and Table II confirms the learned predictor is much better than no prediction at estimating the delay-induced state change. The practical value for soft robot control is real.

Where it gets soft: the stress-test lands. The training target is x(t) - x(t-d), which for smooth trajectories is approximately d·ẋ(t). The input features include the observer-estimated velocity. So KRLST can approximate the fixed linear predictor d·ẋ̂ almost trivially. Without running the controller with that linear predictor as a baseline, you can't tell whether the learning is doing anything beyond phase-lead. That is a load-bearing omission because the paper's claimed novelty is learning the nonlinear SP integral. If the linear predictor gets the same tracking performance, the contribution shrinks to "velocity feedforward works," which is not new. This needs to be fixed before I'd sign off.

The other issues are minor: KRLST hyperparameters (σ², ν, λ, M) are described vaguely in the tuning process, the number of excluded runs isn't quantified, and there's no code or data. Those are addressable. The fixed-delay assumption is fine for this setup.

Bottom line: worth sending to review, but the referee should insist on the linear-predictor control baseline and full parameter reporting. I'd accept, conditional on that.

Referee Report

4 major / 5 minor

Summary. The paper proposes a learning-based approximation of a nonlinear Smith predictor to compensate for a fixed 0.14-second input-output delay in a two-module soft robotic arm. The method combines a Legendre Delay Network (LDN) for compressing input history with a Kernel Recursive Least Squares Tracker (KRLST) for online nonlinear function approximation. The predicted pose is formed by adding the learned correction to the measured pose, and the result is used in the error calculation of a super-twisting sliding-mode controller (STSMC). Experiments on a physical robot show that the learning-based variants reduce XY RMS tracking error compared to the baseline STSMC, with reductions up to 42% at medium gains and 64% at high gains in the stable phase, with statistical significance reported via ANOVA and Tukey post-hoc tests. The paper also presents separate modeling errors showing that the learned predictor reduces the delay-period pose-change prediction error relative to a No-Prediction baseline.

Significance. If the central claim is established, the paper makes a useful engineering contribution: an online, computationally efficient delay-compensation layer that improves tracking of a soft robot without requiring an accurate analytical model. The strongest elements are the physical experiments with 20 runs per condition, the use of ANOVA and Tukey tests to support the main comparisons, and the direct measurement of prediction error (Table II) that quantifies the learned predictor's accuracy. The method's ability to maintain stable tracking at higher controller gains is practically relevant for assistive robotics. However, the paper does not isolate the nonlinear learned component from a much simpler linear predictor, and several experimental details needed for replication are missing; these issues must be addressed before the central claim can be accepted.

major comments (4)

[Section IV-C, Table I] The paper does not compare the proposed learning-based predictor against a simple linear predictor of the form x_p(t) = x(t) + d * x_hat_dot(t). Since the KRLST input includes the observed pose and observer-estimated velocity (Section IV-B), and the training target is x(t) - x(t-d) (Section IV-C), which for smooth trajectories is approximately d * x_dot(t), the learned mapping could trivially approximate this linear predictor. Without a control experiment using that linear predictor, the reported 42-64% improvements in Table I cannot be attributed to the nonlinear learned Smith predictor; they may only demonstrate the benefit of generic phase-lead delay compensation. I request this baseline to be added and discussed.
[Section V-B] The paper states that KRLST parameters sigma^2, nu, lambda, and the dictionary size M were selected through a systematic tuning process, but it does not report the actual parameter values or ranges, only that adjustments of 20-50% were made around initial values. This omission prevents replication and makes it impossible to assess the sensitivity of the reported 64% improvement to hyperparameter choices. The specific values used in the final experiments should be listed.
[Section V-C] The experimental protocol says that runs showing 'anomalous behavior due to sporadic computer lag or external disturbances' were excluded, and that 20 successful experiments were retained per condition. The number of excluded runs is not reported. Without this count, the possibility of selection bias in the retained runs cannot be evaluated. Please quantify the exclusions and state the exclusion criteria more precisely.
[Section III-D] The derivation of the nonlinear Smith predictor contains an inconsistent approximation: the text states 'We also establish Xp(t-d) ≈ X(t), leveraging the fact that an ideal predictor at t-d would have predicted the state at time t.' For an ideal predictor, Xp(t-d) should be the actual state at t-d, not the state at t; the state at t is the predicted output at the end of the integration window, not the initial condition. Since the integral in Eq. (4) is never computed and the learning target uses the actual x(t) - x(t-d), this may be a conceptual error that does not affect the implementation, but it should be corrected or clarified because it is part of the method's motivation.

minor comments (5)

[Abstract and Section II-C] The acronym for Kernel Recursive Least Squares Tracker is written as 'KRLST' in most places but as 'KRSLT' in the abstract and in Section II-C; please make the spelling consistent.
[Section IV-C] The word 'analitically' should be 'analytically'.
[Section VI-B] The sentence 'the improved modeling of LDN-3 during the transient phase may explain its slightly advantage at tracking' should read 'its slight advantage at tracking'.
[Section V-C] The definition of the No-Pred case as 'the difference between current and future states' is confusing, because the training target used in Section IV-C is a past pose difference x(t) - x(t-d). Clarify the time indexing used for the No-Pred error in Table II.
[Table I and Table II] The tables report only the mean ± standard error. Reporting the standard deviation or confidence intervals for the RMS errors would help readers assess the dispersion across the 20 runs.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the learned Smith-predictor term is trained on delayed pose differences and evaluated on independent tracking error, so the central claim does not reduce to its inputs.

full rationale

The paper's derivation chain is self-contained and empirical. The KRLST predictor is trained online with buffered data {X(t-d), u[t-2d,t-d]} and target x(t)-x(t-d) (Sections IV-B and IV-C), and at inference it maps {X(t), u[t-d,t]} to an estimate yhat(t) of the future pose change over the delay. This is a supervised forward-prediction task, not an identity: the training target is a measured state difference over the delay, while the headline result is the XY RMS tracking error relative to the reference trajectory (Table I), an independent control-performance metric. The modeling error in Table II compares the learned yhat with actual future state changes and includes a No-Pred baseline of |x(t+d)-x(t)|, so even the secondary metric is predictive rather than an in-sample fit. The only notable self-citation is [12], which supplies the baseline STSMC and the SINDYc-identified model; this is a comparison baseline, not a theorem used to force the claimed improvement. No uniqueness claim, ansatz smuggled via citation, or renaming of a known result is present. The skeptical concern that a simple linear predictor might achieve a similar 64% improvement is a question about whether an additional control baseline was tested, not evidence that the paper's derivation is circular.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. LDN states, KRLST dictionaries, and Smith predictor terms are all from prior literature.

free parameters (7)

KRLST kernel width sigma^2 = not reported
Tuned offline on prerecorded data and refined online with 20-50% adjustments (Section V-B). Affects the learned predictor's generalization and is not disclosed.
KRLST regularization nu = not reported
Selected in the same two-stage tuning process (Section V-B); prevents overfitting in the kernel regression.
KRLST forgetting factor lambda = not reported
Controls how quickly the model adapts to non-stationary dynamics; tuned in the same process.
KRLST dictionary size M = 80
Cap chosen to maintain computational efficiency (Section V-B).
LDN polynomial order p = 3
Chosen as a balance between memory compression and accuracy (Section IV-A).
Baseline controller gains (k1, k2, Gamma) = not specified
Tuned experimentally to define low, medium, and high gain conditions (Section V-B). The comparison fairness depends on these gains.
Observer gain L = not specified
Used to estimate velocity from magnetic tracker pose (Section III-D); value not reported.

assumptions (5)

domain assumption The first-order velocity model from [12] (Equation 1) adequately represents the two-module arm for control.
Section III-B applies the single-module SINDYc model to a two-module system, acknowledging increased inertia and coupling.
domain assumption The system delay is constant and exactly 0.14 seconds.
Sections III-C and IV-A set the LDN memory and training window equal to this fixed delay; time-varying delays are not considered.
ad hoc to paper The measured pose difference x(t) - x(t-d) is a valid approximation of the Smith predictor integral.
Section IV-C uses this finite difference as the training target; the ideal integral in Equation 4 is defined through predicted states, which may differ from measurements.
standard math KRLST convergence and forgetting properties hold as described in [11].
Section IV-B relies on the online adaptation and dictionary management of KRLST.
domain assumption An LDN with p=3 states can represent the relevant input history over the delay window.
Section IV-A chooses p=3 based on [10]; the paper provides empirical comparison but no guarantee for all inputs.

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Cite this review

Pith. "Pith review of Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots." pith.science (2026). https://pith.science/paper/44OJNOO2

@misc{pith2026250412428,
  author       = {Pith},
  title        = {Pith review of: Learning-based Delay Compensation for Enhanced Control of Assistive Soft Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/44OJNOO2}},
  note         = {Machine review of arXiv:2504.12428}
}

read the original abstract

Soft robots are increasingly used in healthcare, especially for assistive care, due to their inherent safety and adaptability. Controlling soft robots is challenging due to their nonlinear dynamics and the presence of time delays, especially in applications like a soft robotic arm for patient care. This paper presents a learning-based approach to approximate the nonlinear state predictor (Smith Predictor), aiming to improve tracking performance in a two-module soft robot arm with a short inherent input delay. The method uses Kernel Recursive Least Squares Tracker (KRLST) for online learning of the system dynamics and a Legendre Delay Network (LDN) to compress past input history for efficient delay compensation. Experimental results demonstrate significant improvement in tracking performance compared to a baseline model-based non-linear controller. Statistical analysis confirms the significance of the improvements. The method is computationally efficient and adaptable online, making it suitable for real-world scenarios and highlighting its potential for enabling safer and more accurate control of soft robots in assistive care applications.

Figures

Figures reproduced from arXiv: 2504.12428 by the authors.

**Figure 1.** Soft robot arm with sensor and actuator setup. The cross-section shows the distribution of the three cables (C1, C2, C3) in one of the modules. III. SYSTEM DESCRIPTION A. Soft Robot Arm The soft robot arm consists of two identical modules connected in series, each actuated by three servomotors for cable-driven control. The use of this arm for assistive applications in elderly care was explored in [32]. A magnetic … view at source ↗

**Figure 2.** Control architecture combining the model-based bas [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Learning Predictor, showing the dependency on the var ˙ [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Comparison of XY tracking error (RMS) between baseli [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Comparison of XY prediction error magnitude across g [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 31 canonical work pages

[1]

World report on ageing and health,

W. H. Organization et al. , “World report on ageing and health,” 2015

work page 2015
[2]

Caregiver burden and nursing home admission of frail elderly persons,

S. McFall and B. H. Miller, “Caregiver burden and nursing home admission of frail elderly persons,” Journals of Gerontology , vol. 47, 1992

work page 1992
[3]

M. S. Porta, S. Greenland, M. Hernán, I. dos Santos Silva, and J. M. Last, A dictionary of epidemiology . Oxford university press, 2014

work page 2014
[4]

Bi omedical applications of soft robotics,

M. Cianchetti, C. Laschi, A. Menciassi, and P . Dario, “Bi omedical applications of soft robotics,” 2018

work page 2018
[5]

Design, fabrication and control of soft robots,

D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” 2015

work page 2015
[6]

Continuous backbone “continuum

I. D. Walker, “Continuous backbone “continuum” robot ma nipulators,” International Scholarly Research Notices , vol. 2013, no. 1, p. 726506, 2013

work page 2013
[7]

J. H. Park, T. H. Lee, Y . Liu, and J. Chen, Dynamic Systems with Time Delays: Stability and Control , 2019

work page 2019
[8]

Q. C. Zhong, Robust control of time-delay systems , 2006

work page 2006

Show all 32 references

[9]

A process-model control for line ar systems with delay,

K. Watanabe and M. Ito, “A process-model control for line ar systems with delay,” IEEE Transactions on Automatic Control , vol. 26, 1981

1981
[10]

Legendre mem ory units: Continuous-time representation in recurrent neural netwo rks,

A. R. V oelker, I. Kajic, and C. Eliasmith, “Legendre mem ory units: Continuous-time representation in recurrent neural netwo rks,” in Ad- vances in Neural Information Processing Systems , vol. 32, 2019

2019
[11]

Kernel recursive least-squares tracker for time-varying regress ion,

S. V . V aerenbergh, M. Lazaro-Gredilla, and I. Santamar ia, “Kernel recursive least-squares tracker for time-varying regress ion,” IEEE Transactions on Neural Networks and Learning Systems , vol. 23, 2012

2012
[12]

Sliding-mode control of a soft robot based on data-driven s parse identiﬁcation,

D. Papageorgiou, G. Þóra Sigurðardóttir, E. Falotico, and S. Tolu, “Sliding-mode control of a soft robot based on data-driven s parse identiﬁcation,” Control Engineering Practice , vol. 144, 2024

2024
[13]

Kinematics and the implem entation of an elephant’s trunk manipulator and other continuum styl e robots,

M. W. Hannan and I. D. Walker, “Kinematics and the implem entation of an elephant’s trunk manipulator and other continuum styl e robots,” Journal of Robotic Systems , vol. 20, 2003

2003
[14]

Dynamic modeling of constant curvature contin uum robots using the euler-lagrange formalism,

V . Falkenhahn, T. Mahl, A. Hildebrandt, R. Neumann, and O. Sawodny, “Dynamic modeling of constant curvature contin uum robots using the euler-lagrange formalism,” in IEEE International Conference on Intelligent Robots and Systems , 2014

2014
[15]

Neural networks based approach for inverse kinematic mode ling of a compact bionic handling assistant trunk,

A. Melingui, R. Merzouki, J. B. Mbede, C. Escande, and N. Benoudjit, “Neural networks based approach for inverse kinematic mode ling of a compact bionic handling assistant trunk,” in IEEE International Symposium on Industrial Electronics , 2014

2014
[16]

L earning dynamic models for open loop predictive control of soft robo tic manipulators,

T. G. Thuruthel, E. Falotico, F. Renda, and C. Laschi, “L earning dynamic models for open loop predictive control of soft robo tic manipulators,” Bioinspiration and Biomimetics , vol. 12, 2017

2017
[17]

Hybrid anal ytical and data- driven modeling for feed-forward robot control†,

R. F. Reinhart, Z. Shareef, and J. J. Steil, “Hybrid anal ytical and data- driven modeling for feed-forward robot control†,” Sensors (Switzer- land), vol. 17, 2017

2017
[18]

Control strategies for soft robotic manipulators: A survey,

T. G. Thuruthel, Y . Ansari, E. Falotico, and C. Laschi, “ Control strategies for soft robotic manipulators: A survey,” 2018

2018
[19]

A survey for machine learn ing- based control of continuum robots,

X. Wang, Y . Li, and K. W. Kwok, “A survey for machine learn ing- based control of continuum robots,” 2021

2021
[20]

Krstic, Delay compensation for nonlinear , adaptive, and PDE Systems, 2009, vol

M. Krstic, Delay compensation for nonlinear , adaptive, and PDE Systems, 2009, vol. 0

2009
[21]

Saturat ed control of an uncertain nonlinear system with input delay,

N. Fischer, A. Dani, N. Sharma, and W. E. Dixon, “Saturat ed control of an uncertain nonlinear system with input delay,” Automatica, vol. 49, 2013

2013
[22]

Iterative learning contro l design for smith predictor,

Q. Hu, J. X. Xu, and T. H. Lee, “Iterative learning contro l design for smith predictor,” Systems and Control Letters , vol. 44, 2001

2001
[23]

Two-dimensional delay compensation based ite rative learning control scheme for batch processes with both input and state delays,

S. Hao, “Two-dimensional delay compensation based ite rative learning control scheme for batch processes with both input and state delays,” Journal of the Franklin Institute , vol. 356, 2019

2019
[24]

Time-delay c om- pensation method for networked control system based on time -delay prediction and implicit pigpc,

Z. D. Tian, X. W. Gao, B. L. Gong, and T. Shi, “Time-delay c om- pensation method for networked control system based on time -delay prediction and implicit pigpc,” International Journal of Automation and Computing , vol. 12, 2015

2015
[25]

Deep learning-based smith predictor design for a remote graspin g control system,

D. E. Kim, A. Li, M. N. Dau, H. H. Kim, and W. Y . Chung, “Deep learning-based smith predictor design for a remote graspin g control system,” Journal of Mechanical Science and Technology, vol. 36, 2022

2022
[26]

Machine learning-based predictive control of nonlinear time-delay systems: Closed-loop stability and input delay compensa- tion,

A. Alnajdi, A. Suryavanshi, M. S. Alhajeri, F. Abdullah , and P . D. Christoﬁdes, “Machine learning-based predictive control of nonlinear time-delay systems: Closed-loop stability and input delay compensa- tion,” Digital Chemical Engineering , vol. 7, 2023

2023
[27]

A novel error-compe nsation control for a class of high-order nonlinear systems with inp ut delay,

C. Shi, Z. Liu, X. Dong, and Y . Chen, “A novel error-compe nsation control for a class of high-order nonlinear systems with inp ut delay,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, 2018

2018
[28]

Scholkopf and A

B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2018

2018
[29]

Kernel methods for the approxi mation of nonlinear systems,

J. Bouvrie and B. Hamzi, “Kernel methods for the approxi mation of nonlinear systems,” SIAM Journal on Control and Optimization , vol. 55, 2017

2017
[30]

Computing time delay and its effec ts on real-time control systems,

K. G. Shin and X. Cui, “Computing time delay and its effec ts on real-time control systems,” IEEE Transactions on Control Systems Technology, vol. 3, 1995

1995
[31]

Lmuformer: Low complexity yet powerful spiking model with legendre memory units,

Z. Liu, G. Datta, A. Li, and P . A. Beerel, “Lmuformer: Low complexity yet powerful spiking model with legendre memory units,” arXiv preprint arXiv:2402.04882, 2024

2024 arXiv
[32]

I-support: A r obotic platform of an assistive bathing robot for the elderly popul ation,

A. Zlatintsi, A. C. Dometios, N. Kardaris, I. Rodomagou lakis, P . Koutras, X. Papageorgiou, P . Maragos, C. S. Tzafestas, P . V artholomeos, K. Hauer, C. Werner, R. Annicchiarico, M. G . Lom- bardi, F. Adriano, T. Asfour, A. M. Sabatini, C. Laschi, M. Ci anchetti, A. Güler, I...

2020

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

World report on ageing and health,

W. H. Organization et al. , “World report on ageing and health,” 2015

work page 2015

[2] [2]

Caregiver burden and nursing home admission of frail elderly persons,

S. McFall and B. H. Miller, “Caregiver burden and nursing home admission of frail elderly persons,” Journals of Gerontology , vol. 47, 1992

work page 1992

[3] [3]

M. S. Porta, S. Greenland, M. Hernán, I. dos Santos Silva, and J. M. Last, A dictionary of epidemiology . Oxford university press, 2014

work page 2014

[4] [4]

Bi omedical applications of soft robotics,

M. Cianchetti, C. Laschi, A. Menciassi, and P . Dario, “Bi omedical applications of soft robotics,” 2018

work page 2018

[5] [5]

Design, fabrication and control of soft robots,

D. Rus and M. T. Tolley, “Design, fabrication and control of soft robots,” 2015

work page 2015

[6] [6]

Continuous backbone “continuum

I. D. Walker, “Continuous backbone “continuum” robot ma nipulators,” International Scholarly Research Notices , vol. 2013, no. 1, p. 726506, 2013

work page 2013

[7] [7]

J. H. Park, T. H. Lee, Y . Liu, and J. Chen, Dynamic Systems with Time Delays: Stability and Control , 2019

work page 2019

[8] [8]

Q. C. Zhong, Robust control of time-delay systems , 2006

work page 2006