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Novel Model-free Optimal Active Vibration Control Strategy Based on Deep Reinforcement Learning

Zhang, Yi-Ang ; Zhu, Songye Ikeda, Yoshiki

Structural control and health monitoring, 2023-02, Vol.2023, p.1-15 [Periódico revisado por pares]

Hindawi

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  • Título:
    Novel Model-free Optimal Active Vibration Control Strategy Based on Deep Reinforcement Learning
  • Autor: Zhang, Yi-Ang ; Zhu, Songye
  • Ikeda, Yoshiki
  • É parte de: Structural control and health monitoring, 2023-02, Vol.2023, p.1-15
  • Descrição: Neural networks (NNs) can provide a simple solution to complex structural vibration control problems. However, most past NN-based control strategies cannot guarantee an optimal policy in structural vibration control. In this study, a novel active vibration control strategy based on deep reinforcement learning is proposed, which utilizes the learning ability of NN controllers and simultaneously provides control performance comparable to traditional model-based optimal controllers. The proposed learning algorithm can determine the control policy through interaction with the environment without knowing dynamic system models. This study shows that the proposed model-free strategy can provide optimal control performance to various systems and excitations. The proposed control strategy is first verified on a single-degree-of-freedom model and subsequently extended to a multi-degree-of-freedom shear-building model. Its control performance with full-state feedback is nearly the same as that of a classical linear quadratic regulator. Moreover, the learned policy can outperform a traditional output feedback controller in a partially observed system. The robustness of the proposed control strategy against measurement noise is also tested.
  • Editor: Hindawi
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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