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受約束空間機器人降階自適應神經(jīng)網(wǎng)絡(luò )滑模控制
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中國科學(xué)院西安光學(xué)精密機械研究所

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The Reduced Order to the Adaptive Neural Network Sliding Mode Control of Constrained Space Robot
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    摘要:

    為了實(shí)現受約束空間機器人的高精度控制,提出了一種基于U-K(Udwadia-Kalaba)方程的降階自適應神經(jīng)網(wǎng)絡(luò )滑模控制算法。首先,基于U-K方程,同時(shí)考慮受約束空間機器人末端的理想約束力與非理想約束力,推導得到詳細的動(dòng)力學(xué)方程。接著(zhù),考慮到非理想約束力(主要是受約束空間機器人末端受到的切向力)具有不確定性且單獨采用滑模控制會(huì )出現抖振現象,提出了自適應神經(jīng)網(wǎng)絡(luò )滑模控制算法,實(shí)現各關(guān)節角度、角速度以及非理想約束力的高精度跟蹤。然后,針對系統受約束模型,對動(dòng)力學(xué)方程和滑模控制器進(jìn)行了降階求解,減少了變量并簡(jiǎn)化了計算過(guò)程。最后,為了驗證所提算法的正確性與合理性,以2自由度受約束空間機器人為例進(jìn)行了仿真驗證。仿真結果表明:受約束空間機器人的各關(guān)節角度、角速度以及非理想約束力的跟蹤誤差均低于10-4量級。

    Abstract:

    In order to achieve high precision control of the constrained space robot, a reduced order adaptive neural network sliding mode control algorithm based on U-K (Udwadia-Kalaba) equation is proposed. Firstly, On the basis of the U-K equation and considering the ideal and non-ideal constrained forces at the terminal of the constrained space robot, the detailed dynamic equations are derived. Secondly, considering the uncertainty of the non-ideal constrained force (mainly the tangential force on the terminal of the constrained space robot) and the chattering phenomenon when using sliding mode control alone, the adaptive neural network sliding mode control algorithm is proposed to realize the high-precision tracking of each joint angle, angle speed and the non-ideal constrained force. Thirdly, for the constrained model of the system, the dynamic equation and the sliding mode controller are reduced to decrease the variables and simplify the calculation process. Finally, in order to verify the correctness and rationality of the proposed algorithm, the 2-DOF constrained space robot is taken as the simulated object. The simulation results show that the tracking errors of joint angles, angle speed and the non-ideal constrained force are less than 10-4 order of magnitude.

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師恒,王雪莉,謝梅林,曹鈺,馮旭斌,廉學(xué)正.受約束空間機器人降階自適應神經(jīng)網(wǎng)絡(luò )滑模控制計算機測量與控制[J].,2023,31(12):103-109.

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歷史
  • 收稿日期:2022-11-24
  • 最后修改日期:2023-03-06
  • 錄用日期:2023-03-06
  • 在線(xiàn)發(fā)布日期: 2023-12-27
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