Adaptive Input Shaping for Flexible Systems Using an Extreme Learning Machine Algorithm Identification

Hu, Jun; Chu, Zhongyi

doi:10.1007/978-3-319-28397-5_17

Cited by 1 publication

(2 citation statements)

References 17 publications

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“…Among these pulses, there are only D nonzero pulses, denoted as A = {A 1 , A 2 , • • • , A D } and t = {t 1 , t 2 , • • • , t D }. Hence, the discrete expression of H can be expressed as (19):…”

Section: Adaptive Adjustment Of Shaper Parametersmentioning

confidence: 99%

“…In [18], the pulse amplitude and time of a ZV shaper were iteratively updated in real time according to the constructed performance index and the measured output and control data. Moreover, an adaptive input-shaping method based on an extreme learning machine (ELM) was proposed in [19]. Specifically, the pulse response sequence of the closed-loop system was identified and fitted using an online frequency ELM algorithm, and the shaper parameters were updated in real time.…”

Section: Introductionmentioning

confidence: 99%

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Optimization-Based Input-Shaping Swing Control of Overhead Cranes

Tang,

Ma,

Wang

et al. 2023

Applied Sciences

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A novel swing control scheme combining optimization and input-shaping techniques is proposed for overhead cranes subjected to parameter variations and modeling errors. An input shaper was first designed using the analytical method based on the linear swing dynamic model. Then, the particle swarm optimization algorithm was used to optimize the pulse amplitudes and time of the shaper to reduce the influence of modeling errors on the residual vibration. Furthermore, an adaptive optimization method was also used to optimize the parameters of the shaper to suppress the influence of the change in the payload mass and the rope length on the residual vibration. The proposed control scheme can suppress the influence of uncertainties on residual vibration and improve the anti-disturbance ability of a closed-loop system via offline and online dual optimization. Finally, the simulation results verify the effectiveness of the scheme.

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Section: Adaptive Adjustment Of Shaper Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%