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Output feedback control for free-floating space robotic manipulators base on adaptive fuzzy neural network

机译:基于自适应模糊神经网络的自由浮动空间机械臂输出反馈控制

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摘要

Free-floating space robotic manipulators with uncertainty of model and external disturbances are considered by the paper. Joints motors of space manipulators usually work at low-velocity operating conditions, tachometers are difficult to satisfy signal request. The paper brings forward a model-free fuzzy basis function network output feedback control strategy of space robot base on velocity reconstructed. Firstly, space robot dynamic model is established, and then velocity signals are reconstructed by observer, the fuzzy basis function network is used to approximate nonlinear function of observer and controller and compensated the effect of system deviations to the velocity reconstruction. The network weights and the parameters can be tuned online by hybrid back-propagation algorithm and e-correction algorithm, with no off-line learning phase required. The proposed observer and controller do not require any space robot dynamic model (including the inertia matrix inverse). Based on Lyapunov theory, the paper proves that the whole closed-loop system is uniformly ultimately bounded (UUB). Comparison results further show the proposed control method of superiority comparison. Simulation results verify that the observer-based output feedback control strategy can achieve higher precision, and has important engineering value to uncertainty space robots without velocity information.
机译:本文考虑了具有模型不确定性和外部干扰的自由浮动空间机械手。空间机械手的关节电动机通常在低速运行条件下工作,转速表很难满足信号要求。提出了基于速度重构的空间机器人无模型模糊基函数网络输出反馈控制策略。首先建立空间机器人动力学模型,然后由观测器重构速度信号,利用模糊基函数网络逼近观测器和控制器的非线性函数,并补偿系统偏差对速度重构的影响。可以通过混合反向传播算法和电子校正算法在线调整网络权重和参数,而无需离线学习阶段。提出的观察者和控制器不需要任何空间机器人动力学模型(包括惯性矩阵逆)。基于李雅普诺夫理论,证明了整个闭环系统是统一有界的。比较结果进一步表明了所提出的优势比较控制方法。仿真结果表明,基于观测器的输出反馈控制策略可以达到较高的精度,对于没有速度信息的不确定空间机器人具有重要的工程价值。

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