What does this research mean for the field?

An integrated hierarchical strategy combining Stochastic Trajectory Optimization Model Predictive Control (STO-MPC) and Adaptive Disturbance Observer-Rapid Integral Terminal Sliding Mode Control (ADO-RITSMC) significantly improves trajectory tracking accuracy, reduces computation time, and suppresses residual vibration in continuum flexible robotic arms. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To improve trajectory tracking accuracy and suppress flexible vibrations in cable-driven continuum flexible manipulators subject to computational delays and dynamic disturbances.

August 29, 2026Journal of VibroengineeringOpen Access

Optimization of trajectory tracking accuracy and vibration suppression for continuum flexible robotic arm

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Authors

HSHaiyan SunQingdao Huanghai University YLYuanyuan LiQingdao Huanghai University

Discussion

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Implication

Experimental study demonstrates enhanced trajectory tracking and vibration suppression in cable-driven flexible manipulators, suggesting improved dynamic precision for robotic control.

Key Points

To improve trajectory tracking accuracy and suppress flexible vibrations in cable-driven continuum flexible manipulators subject to computational delays and dynamic disturbances.
Constructed a hierarchical framework integrating Stochastic Trajectory Optimization Model Predictive Control (STO-MPC) with probabilistic obstacle avoidance and gradient-independent solving.
Established an Adaptive Disturbance Observer-Rapid Integral Terminal Sliding Mode Control (ADO-RITSMC) framework for model-free compensation and finite-time error convergence.
Conducted comparative tests on standard O-shaped and high-curvature V-shaped trajectories against GO-MPC and APF-MPC algorithms.
STO-MPC achieved the lowest peak computation time, converged obstacle state estimation within 0.5 s, and achieved a steady-state velocity estimation error of 0.0015 m/s.
Trajectory tracking RMSE was 18.7% lower than GO-MPC and 36.8% lower than APF-MPC.
ADO-RITSMC reduced vibration amplitude by 16.7% with a peak vibration acceleration of –1.0 g, accelerating vibration attenuation during dynamic obstacle avoidance.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a9299db8e5d7d1fc0c12285 https://doi.org/10.21595/jve.2026.26644

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August 29, 2026Journal of VibroengineeringOpen Access

Optimization of trajectory tracking accuracy and vibration suppression for continuum flexible robotic arm

View Full Paper

Ask AI

Bookmark

Authors

HSHaiyan SunQingdao Huanghai University YLYuanyuan LiQingdao Huanghai University

Discussion

Member takes

Implication

Experimental study demonstrates enhanced trajectory tracking and vibration suppression in cable-driven flexible manipulators, suggesting improved dynamic precision for robotic control.

Key Points

To improve trajectory tracking accuracy and suppress flexible vibrations in cable-driven continuum flexible manipulators subject to computational delays and dynamic disturbances.
Constructed a hierarchical framework integrating Stochastic Trajectory Optimization Model Predictive Control (STO-MPC) with probabilistic obstacle avoidance and gradient-independent solving.
Established an Adaptive Disturbance Observer-Rapid Integral Terminal Sliding Mode Control (ADO-RITSMC) framework for model-free compensation and finite-time error convergence.
Conducted comparative tests on standard O-shaped and high-curvature V-shaped trajectories against GO-MPC and APF-MPC algorithms.
STO-MPC achieved the lowest peak computation time, converged obstacle state estimation within 0.5 s, and achieved a steady-state velocity estimation error of 0.0015 m/s.
Trajectory tracking RMSE was 18.7% lower than GO-MPC and 36.8% lower than APF-MPC.
ADO-RITSMC reduced vibration amplitude by 16.7% with a peak vibration acceleration of –1.0 g, accelerating vibration attenuation during dynamic obstacle avoidance.

Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/6a9299db8e5d7d1fc0c12285 https://doi.org/10.21595/jve.2026.26644

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