Reinforcement learning vibration control for a flexible hinged plate

Qiu, Zhi-cheng; Chen, Guohao; Zhang, Xianmin

doi:10.1016/j.ast.2021.107056

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…Qiu et al [141] carried out bending and torsional vibration control via an RL algorithm virtually trained with a validated FEM model and transferred to an experimental setup where it shows better performance than PD control. The vibration control of a rotating machine was also performed through RLC using pad actuators [142].…”

Section: Driven Control Designmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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Section: Driven Control Designmentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Although pioneering, they concern only active control or structures with a very limited number of degrees of freedom. Qiu et al (2021) adopted a deep deterministic policy gradient RL algorithm to train the neural networks that are responsible for controlling a flexible hinged plate. The control was realized through piezoelectric actuators.…”

Section: Rl Approachesmentioning

confidence: 99%

“…Qiu et al. (2021) adopted a deep deterministic policy gradient RL algorithm to train the neural networks that are responsible for controlling a flexible hinged plate. The control was realized through piezoelectric actuators.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning‐based control to suppress the transient vibration of semi‐active structures subjected to unknown harmonic excitation

Pisarski

Jankowski

2022

Computer aided Civil Eng

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The problem of adaptive semi-active control of transient structural vibration induced by unknown harmonic excitation is studied. The controller adaptation is attained by using a specially designed reinforcement learning algorithm that adjusts the parameters of a switching control policy to guarantee efficient dissipation of the structural energy. This algorithm relies on an efficient gradient-based sequence that accelerates the learning protocol and results in suboptimal control. The performance of this method is examined through numerical experiments for a span structure that is equipped with a semi-active device of controlled stiffness and damping parameters.The experiments cover a selection of control learning scenarios and comparisons to optimal open-loop and heuristic state-feedback control strategies. This study has confirmed that the developed method has high stabilizing performance, and the relatively low computational burden of the incorporated iterative learning algorithm facilitates its application to multi-degree-of-freedom structures.

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“…Actually, RL has been widely used for building agents to learn complex control in complex environments, and it has achieved some successes in a variety of domains [11][12][13][14][15]. Moreover, its applicability has been extended to the vibration-control domain, such as the vibration control of the suspension [16][17][18][19], manipulator [20][21][22], magneto rheological damper [23,24], flexible beam/plate [25][26][27], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In the vibration-control problems of flexible beams or plates [25][26][27], accurate finite element models are constructed as the simulation environment, and then reinforcementlearning algorithms, such as the soft actor-critic algorithm, DDPG algorithm and multiagent twin delayed DDPG algorithm, are applied to train the vibration controllers. Finally, the well-trained controllers are validated in experiments.…”

Section: Introductionmentioning

confidence: 99%

A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental Study

Feng

Chen

et al. 2022

Applied Sciences

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High-dimensional high-frequency continuous-vibration control problems often have very complex dynamic behaviors. It is difficult for the conventional control methods to obtain appropriate control laws from such complex systems to suppress the vibration. This paper proposes a new vibration controller by using reinforcement learning (RL) and a finite-impulse-response (FIR) filter. First, a simulator with enough physical fidelity was built for the vibration system. Then, the deep deterministic policy gradient (DDPG) algorithm interacted with the simulator to find a near-optimal control policy to meet the specified goals. Finally, the control policy, represented as a neural network, was run directly on a controller in real-world experiments with high-dimensional and high-frequency dynamics. The simulation results show that the maximum peak values of the power-spectrum-density (PSD) curves at specific frequencies can be reduced by over 63%. The experimental results show that the peak values of the PSD curves at specific frequencies were reduced by more than 47% (maximum over 52%). The numerical and experimental results indicate that the proposed controller can significantly attenuate various vibrations within the range from 50 Hz to 60 Hz.

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Reinforcement learning vibration control for a flexible hinged plate

Cited by 19 publications

References 27 publications

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Reinforcement learning‐based control to suppress the transient vibration of semi‐active structures subjected to unknown harmonic excitation

A New Vibration Controller Design Method Using Reinforcement Learning and FIR Filters: A Numerical and Experimental Study

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