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Intelligent fuzzy weighted input estimation method for the input force on the plate structure

机译:板结构输入力的智能模糊加权输入估计方法

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摘要

The innovative intelligent fuzzy weighted input estimation method which efficiently and robustly estimates the unknown time-varying input force in on-line is presented in this paper. The algorithm includes the Kalman Filter (KF) and the recursive least square estimator (RLSE), which is weighted by the fuzzy weighting factor proposed based on the fuzzy logic inference system. To directly synthesize the Kalman filter with the estimator, this work presents an efficient robust forgetting zone, which is capable of providing a reasonable compromise between the tracking capability and the flexibility against noises. The capability of this inverse method are demonstrated in the input force estimation cases of the plate structure system. The proposed algorithm is further compared by alternating bwtween the constant and adaptive weighting factors. The results show that this method has the properties of faster convergence in the initial response, better target tracking capability, and more effective noise and measurement bias reduction.
机译:本文提出了一种创新的智能模糊加权输入估计方法,该方法可以有效,鲁棒地估计未知的时变输入力。该算法包括卡尔曼滤波器(KF)和递归最小二乘估计器(RLSE),它们由基于模糊逻辑推理系统提出的模糊加权因子加权。为了直接用估计器合成卡尔曼滤波器,这项工作提出了一个有效的健壮的遗忘区,该遗忘区能够在跟踪能力和抗噪声的灵活性之间提供合理的折衷。在板结构系统的输入力估算情况下证明了这种反方法的能力。通过在常数加权因子和自适应加权因子之间交替进行进一步比较所提出的算法。结果表明,该方法具有初始响应收敛速度更快,目标跟踪能力更好,噪声和测量偏差降低更有效的特点。

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