Rapid Vibration Suppression and Trajectory Tracking of a Serial Manipulator with Multi-Flexible Links
- URL: http://arxiv.org/abs/2605.17477v1
- Date: Sun, 17 May 2026 14:30:45 GMT
- Title: Rapid Vibration Suppression and Trajectory Tracking of a Serial Manipulator with Multi-Flexible Links
- Abstract summary: This article presents a backstepping output-feedback framework for fast vibration suppression and tip tracking of an n-degree-of-freedom serial flexible manipulator robot.<n>Experiments on a two-link flexible manipulator demonstrate faster vibration suppression and convergence of the end-effector to the desired trajectory.
- Score: 5.756257025997414
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Flexible robotic manipulators (FRMs) offer advantages in lightweight design and large workspace, but their structural flexibility induces vibrations, accelerates fatigue, degrades tracking performance, and limits operational speed. These challenges are further amplified in multi-link serial manipulators, where increased overall length leads to greater structural flexibility. This article presents a backstepping output-feedback framework for fast vibration suppression and tip tracking of an n-degree-of-freedom serial flexible manipulator robot (nDSFMR), with a DeepONet-based approximation for practical deployment. Each link-joint is modeled as a Timoshenko beam coupled with an ODE and transformed into a canonical hyperbolic PDE with boundary dynamics. A backstepping-based boundary controller at the joint is developed to equivalently inject distributed damping along the beam, enabling rapid vibration suppression and trajectory tracking, only using available boundary measurements. To enable real-time implementation and scalability, a DeepONet neural operator is introduced to approximate the backstepping kernels, significantly reducing computational cost and facilitating fast controller updates under varying operating conditions. Experiments on a two-link flexible manipulator demonstrate faster vibration suppression and convergence of the end-effector to the desired trajectory, compared with a linear quadratic regulator (LQR) with feedforward control.
Related papers
- Contact-Aware Incremental Model Predictive Control for an Underactuated Aerial Manipulator [0.9328991021103293]
We present a robust contact-aware control framework for aerial writing on an underactuated platform.<n>The proposed controllers are validated on a quadrotor-based aerial manipulator with a rigid, single-link, one-degree-of-freedom (DoF) arm.
arXiv Detail & Related papers (2026-09-10T15:03:02Z) - WaveZip: Wavelet-Driven Space-Time Decoupling for Video Token Condensation [69.37452950816122]
WaveZip is a joint signal-frequency-domain framework for efficient video inference.<n> experiments on long video understanding benchmarks demonstrate that WaveZip retains 99.6% of the full performance.
arXiv Detail & Related papers (2026-07-25T16:05:22Z) - A Task-Space Receding Horizon Controller for Fast Collision Avoidance [0.08594140167290099]
Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion.<n>Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints.<n>We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference.
arXiv Detail & Related papers (2026-07-17T08:13:12Z) - T-Rex: Tactile-Reactive Dexterous Manipulation [139.71263755530654]
We introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder.<n>We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation.
arXiv Detail & Related papers (2026-06-15T17:59:55Z) - TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies [58.40033352838586]
Existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations.<n>We observe that the magnitude of each predicted action already governs how fast the robot moves.<n>We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition.
arXiv Detail & Related papers (2026-06-04T17:59:40Z) - Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control [50.28263951510334]
We propose a hybrid hierarchical control framework for reactive dexterous grasping.<n>The proposed framework explicitly decouples high-level spatial intent from low-level joint execution.<n>We extensively validate the proposed framework through a rigorous simulation-to-reality pipeline.
arXiv Detail & Related papers (2026-05-05T04:49:38Z) - Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions [2.747173908186719]
Deep Reinforcement Learning (DRL) has emerged as a promising method for controlling complex nonlinear dynamics.<n>We train DRL controllers to modulate spatially segmented injection pressure in a one-dimensional reduced-order RDE model.<n>Across a range of actuation periods, initial states, and target modes, controllers trained in the moving frame learn more reliably than those trained in a stationary frame.
arXiv Detail & Related papers (2026-04-15T20:27:56Z) - DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models [61.26694413585136]
Drift-Aware Post-Training Quantization (DA-PTQ) formulates quantization as a drift-aware optimization problem over sequential decision processes.<n>DA-PTQ significantly reduces kinematic drift and achieves comparable performance to full-precision models under low-bit settings.
arXiv Detail & Related papers (2026-04-13T14:51:43Z) - Reinforcement Learning for Fast and Robust Longitudinal Qubit Readout [13.734433692881305]
We develop a reinforcement learning framework to optimize the longitudinal coupling waveform.<n>At a fixed short readout time, the optimized pulse converges to a constraint saturating flat-top protocol.<n>The protocol attains substantial performance gains and yields smooth, hardware-compatible waveforms governed by an interpretable saturate-and-hold'' mechanism.
arXiv Detail & Related papers (2026-03-18T01:27:09Z) - Reconciling distributed compliance with high-performance control in continuum soft robotics [5.559127468747317]
We present a highly compliant, fully continuum robotic arm without hardware discretization or stiffness-based mode suppression.<n>The platform integrates direct-drive actuation, a tendon routing scheme enabling coupled bending and twisting, and a structured nonlinear control architecture.<n>The proposed system achieves the fastest reported task-execution speed among soft robots.
arXiv Detail & Related papers (2026-03-17T15:01:04Z) - Receding-Horizon Nullspace Optimization for Actuation-Aware Control Allocation in Omnidirectional UAVs [2.573972255510259]
Fully actuated omnidirectional UAVs enable independent control of forces and torques along all six degrees of freedom.<n> conventional control allocation methods neglect the asymmetric dynamics of the onboard actuators.<n>This work proposes a receding-horizon, actuation-aware allocation strategy that explicitly incorporates asymmetric motor dynamics.
arXiv Detail & Related papers (2026-03-06T19:47:45Z) - Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment [92.57576987521107]
We propose a novel unifiedtransform framework with dual-domain progressive temporal alignment and quality-conditioned mixture-of-expert (QCMoE)<n>QCMoE allows continuous and consistent rate control with appealing R-D performance.<n> Experimental results show that the proposed method achieves competitive R-D performance compared with the state-of-the-arts.
arXiv Detail & Related papers (2025-12-11T09:14:51Z) - MIORe & VAR-MIORe: Benchmarks to Push the Boundaries of Restoration [53.180212987726556]
We introduce MIORe and VAR-MIORe, two novel multi-task datasets that address critical limitations in current motion restoration benchmarks.<n>Our datasets capture a broad spectrum of motion scenarios, which include complex ego-camera movements, dynamic multi-subject interactions, and depth-dependent blur effects.
arXiv Detail & Related papers (2025-09-08T15:34:31Z) - FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
arXiv Detail & Related papers (2025-05-23T00:07:59Z) - Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves [69.9104427437916]
Multi-generator Wave Energy Converters (WEC) must handle multiple simultaneous waves coming from different directions called spread waves.
These complex devices need controllers with multiple objectives of energy capture efficiency, reduction of structural stress to limit maintenance, and proactive protection against high waves.
In this paper, we explore different function approximations for the policy and critic networks in modeling the sequential nature of the system dynamics.
arXiv Detail & Related papers (2024-04-17T02:04:10Z) - Spinor matterwave control with nanosecond spin-dependent kicks [0.8155575318208631]
Quantum technology relies on rapid control of atomic matterwaves with hyperfine Raman transitions.
These transitions are accompanied by uncompensated dynamic phases and coherent spin-leakages.
We program an adiabatic pulse sequence to overcome these limitations.
arXiv Detail & Related papers (2022-02-20T01:57:32Z)
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.