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- W2463865388 abstract "In this paper, a promoted adaptive input-shaping (AIS) with extreme learning machine (ELM) is presented to get zero residual vibration (ZRV) of severely time-varying flexible systems. Firstly, the ZRV condition and the traditional adaptive input-shaper is reviewed, together with its disadvantages of insufficient adaptability caused by giant amount of data and low-accuracy calculation caused by noise. After that, online sequential-ELM (OS-ELM) algorithm is introduced to identify the impulse response sequences of the flexible system, its fitting impulse response sequences are gotten to update the shaper parameters with fixed length and less noise; therefore, the above-mentioned problems of traditional AIS could be significantly avoided; that is to say, AIS’s adaptability and identification-accuracy could be improved apparently, which means better performance to suppress the residual vibration of the flexible system. Finally, the verification experiments of presented AIS are implemented on a two-links flexible manipulator, which is a classical flexible system with severely time-varying dynamics; the results proves the effectiveness of the presented AIS method for the vibration control of severely time-varying flexible systems." @default.
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- W2463865388 date "2016-01-01" @default.
- W2463865388 modified "2023-09-29" @default.
- W2463865388 title "Adaptive Input Shaping for Flexible Systems Using an Extreme Learning Machine Algorithm Identification" @default.
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- W2463865388 doi "https://doi.org/10.1007/978-3-319-28397-5_17" @default.
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