Modeling and Control of a Pneumatic Morphing Soft Quadrotor based on the SOFA Framework for Dynamic Soft Robotic Simulation

• URL: http://arxiv.org/abs/2605.21031v2
• Date: Fri, 22 May 2026 15:32:36 GMT
• Title: Modeling and Control of a Pneumatic Morphing Soft Quadrotor based on the SOFA Framework for Dynamic Soft Robotic Simulation
• Abstract summary: This article presents a novel SOFA based finite element method for the soft body modeling and the corresponding dynamic simulation and control of a pneumatic morphing soft quadrotor.<n>The proposed modeling preserves the physical interpretability and control structure of traditional quadrotor dynamics.<n>A proportional-integral controller is proposed to study the controlled dynamic behavior and morphing capabilities of the pneumatic arm.
• Score: 1.821201617799525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents a novel SOFA based finite element method for the soft body modeling and the corresponding dynamic simulation and control of a pneumatic morphing soft quadrotor. The proposed modeling preserves the physical interpretability and control structure of traditional quadrotor dynamics, while capturing the complex, time-varying behavior of pneumatically actuated soft arms. In SOFA, the soft pneumatically actuated arms are discretized as a tetrahedral mesh following an elastic material law that produces internal forces adequate to the real dynamic behavior of the body. Pneumatic actuation governed by both periodic and error-based control signals is applied within the internal cavities to analyze the morphing capability. Finally, a proportional-integral controller is proposed to study the controlled dynamic behavior and morphing capabilities of the pneumatic arm, wherein the pneumatic actuation to the soft arm is controlled to achieve the desired target position. The simulation results show the effectiveness of the proposed novel modeling framework and the related controller design.

Related papers

• Control-Oriented Learning for Dynamic Tracking and Stability Analysis of Soft Pneumatic Actuators [0.0]
  Soft pneumatic actuators offer inherent compliance and safe interaction but remain difficult to model and control.<n>We present a control-oriented data-driven modeling and control framework that decomposes actuator behavior into a nonlinear static equilibrium model.<n>Results demonstrate that structured, control-oriented learning provides an accurate and practical framework for soft actuator control.
  arXiv  Detail & Related papers  (2026-08-25T21:39:02Z)
• Design, Modelling and Experimental Evaluation of a Tendon-driven Wrist Abduction-Adduction Mechanism for an upper limb exoskeleton [0.8399688944263844]
  This paper presents a novel single-cable (tendon), torsional-spring-assisted actuation mechanism for wrist abduction-adduction.<n>The mechanism employs a single Bowden cable passively tensioned by a spiral torsional spring to maintain continuous cable tension without antagonistic actuation.<n>The results demonstrate that simulation-informed stiffness selection can effectively guide the design of compact, cable-driven wrist exoskeletons.
  arXiv  Detail & Related papers  (2026-04-21T08:48:23Z)
• Elastomeric Strain Limitation for Design of Soft Pneumatic Actuators [0.0]
  This thesis presents strategies for the design, modeling, and strain-based control of human-safe elastomeric soft pneumatic actuators.<n>We investigate electroadhesive (EA) strain limiters for variable shape generation, rapid force application, and targeted inflation trajectories.<n>We validate theoretical models based on material properties and energy minimization using active learning and automated testing.
  arXiv  Detail & Related papers  (2026-04-03T00:56:14Z)
• DiffWind: Physics-Informed Differentiable Modeling of Wind-Driven Object Dynamics [97.57382055179725]
  We present DiffWind, a physics-informed-iable framework that unifies wind-object interaction, video-based modeling, and forward simulation.<n>Our method significantly outperforms prior dynamic scene modeling approaches in both reconstruction accuracy and simulation fidelity.
  arXiv  Detail & Related papers  (2026-03-10T13:41:24Z)
• Physics-Embedded Neural ODEs for Learning Antagonistic Pneumatic Artificial Muscle Dynamics [2.8169681659450876]
  Pneumatic artificial muscles (PAMs) enable compliant actuation for soft wearable, assistive, and interactive robots.<n>This paper presents a hybrid neural ordinary differential equation framework that embeds physical structure into a learned model of antagonistic PAM dynamics.
  arXiv  Detail & Related papers  (2026-02-27T04:38:22Z)
• Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped [0.0]
  SLOT (Soft Legged Omnidirectional Tetrapod) is a tendon-driven soft quadruped robot with 3D-printed TPU legs.<n>Each leg is modeled as a deformable continuum using discrete Cosserat rod theory.<n>A modular whole-body modeling framework is introduced, in which compliant leg dynamics are represented.
  arXiv  Detail & Related papers  (2026-02-18T11:14:22Z)
• Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamics [51.85385061275941]
  Molecular dynamics (MD) simulations remain the gold standard for studying protein dynamics.<n>Recent generative models have shown promise in accelerating simulations, yet they struggle with long-horizon generation.<n>We present STAR-MD, a scalable diffusion model that generates physically plausible protein trajectories over micro-scale timescales.
  arXiv  Detail & Related papers  (2026-02-02T14:13:28Z)
• 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)
• Dynamic Manipulation of Deformable Objects in 3D: Simulation, Benchmark and Learning Strategy [88.8665000676562]
  Prior methods often simplify the problem to low-speed or 2D settings, limiting their applicability to real-world 3D tasks.<n>To mitigate data scarcity, we introduce a novel simulation framework and benchmark grounded in reduced-order dynamics.<n>We propose Dynamics Informed Diffusion Policy (DIDP), a framework that integrates imitation pretraining with physics-informed test-time adaptation.
  arXiv  Detail & Related papers  (2025-05-23T03:28:25Z)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• Differentiable Simulation of Soft Multi-body Systems [99.4302215142673]
  We develop a top-down matrix assembly algorithm within Projective Dynamics. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes.
  arXiv  Detail & Related papers  (2022-05-03T20:03:22Z)

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.