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Robust Input Shaping Vibration Suppression Control for a Rigid-Flexible Coupling Hoisting Robot Using Deep Reinforcement Learning
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DOI：10.1109/TII.2026.3678043.png)
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Abstract
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The vibration suppression for rigid-flexible coupling hoisting robots (RFCHRs) under compound motion has always been a focus in the engineering. Most vibration suppression control methods are overly complex in design, making them hard to effectively apply. Some methods that are relatively easy to deploy often fail to adapt well to changes in system uncertain parameters and external environmental disturbances, raising concerns about their robustness. To address this problem, a robust input shaping vibration suppression control method based on deep reinforcement learning (DRL) is proposed. Based on real-time environmental state feedback, this method utilizes the strategy gradient optimization capability of the proximal policy optimization with clipping (PPO-Clip) algorithm, enabling real-time updates of the shaper parameters according to changes in system parameters and external environmental. It can achieve faster vibration suppression for RFCHR, while also possessing strong adaptability to system parameter changes and anti-interference capability. Finally, experiments verify its effectiveness and superiority.
Keywords:
Compound motion
deep reinforcement learning (DRL)
input shaper
rigid-flexible coupling hoisting robot (RFCHRs)
vibration suppression
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