Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot

• URL: http://arxiv.org/abs/2605.18720v1
• Date: Mon, 18 May 2026 17:50:22 GMT
• Title: Data-Driven Dynamic Modeling of a Tendon-Actuated Continuum Robot
• Abstract summary: This paper presents a comparative study of data-driven system identification methods, including N4SID, ARX, and SINDYc, for modeling a tendon-actuated continuum robot.<n>Despite the high number of joints, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics.<n>The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.
• Score: 0.5545791216381869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Developing dynamic models for tendon-driven continuum robots is challenging due to their nonlinear, high-dimensional, and friction-dominated dynamics. This paper presents a comparative study of data-driven system identification methods, including N4SID, ARX, and SINDYc, for modeling a tendon-actuated continuum robot with rolling joints developed at CERN. Despite the high number of joints of the robot, experimental analysis reveals that a two-degree-of-freedom dynamic model can accurately capture the system dynamics, owing to strong kinematic dependencies between the joints. The models are validated against experimental data, and used in the design of a model predictive controller, demonstrating their feasibility for real-time control.

Related papers

• Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots [23.655392951446995]
  Tendon-Driven Robots Continuum (TDCRs) pose significant modeling and control challenges due to complex nonlinearities.<n>This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control.
  arXiv  Detail & Related papers  (2026-04-28T14:20:44Z)
• Efficient and Robust Modeling of Nonlinear Mechanical Systems [0.0]
  A new formulation for the dynamic model of nonlinear mechanical systems is proposed.<n>The proposed model is shown to give superior performances in terms of robustness against measurement noise.
  arXiv  Detail & Related papers  (2026-02-06T12:02:20Z)
• Fractional-order Modeling for Nonlinear Soft Actuators via Particle Swarm Optimization [49.34452620841728]
  This paper proposes an innovative modeling framework based on fractional-order differential equations (FODEs) to capture the dynamic behavior of soft materials.<n>The proposed approach effectively represents the complex deformation phenomena inherent in soft actuators.
  arXiv  Detail & Related papers  (2025-12-20T04:46:25Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• Langevin Flows for Modeling Neural Latent Dynamics [81.81271685018284]
  We introduce LangevinFlow, a sequential Variational Auto-Encoder where the time evolution of latent variables is governed by the underdamped Langevin equation.<n>Our approach incorporates physical priors -- such as inertia, damping, a learned potential function, and forces -- to represent both autonomous and non-autonomous processes in neural systems.<n>Our method outperforms state-of-the-art baselines on synthetic neural populations generated by a Lorenz attractor.
  arXiv  Detail & Related papers  (2025-07-15T17:57:48Z)
• Probabilistic Latent Variable Modeling for Dynamic Friction Identification and Estimation [2.638878351659023]
  Identification of dynamic models in robotics is essential to support control design, friction compensation, output torque estimation.<n>We propose to account for unidentified dynamics in the robot joints using latent dynamic states.<n>We use the Expectation-Maximisation (EM) algorithm to find a Likelihood Maximum Estimate (MLE) of the model parameters.
  arXiv  Detail & Related papers  (2024-12-20T10:16:18Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control [16.88250694156719]
  We present a novel hybrid model formulation that enables us to identify fully physically consistent inertial parameters of a rigid body dynamics model. We compare our approach against state-of-the-art inverse dynamics models on a 7 degree of freedom manipulator.
  arXiv  Detail & Related papers  (2022-05-27T07:39:28Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning [17.80270555749689]
  Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for robots. We present two architectures for forward model learning and one for inverse model learning. Both architectures significantly outperform exist-ing model learning frameworks as well as analytical models in terms of prediction performance.
  arXiv  Detail & Related papers  (2020-10-20T11:28:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.