Neural Model Extraction for Model-Based Control of a Neural Network Forward Model
Neural Model Extraction for Model-Based Control of a Neural Network Forward Model
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DOI：
10.1007/s42979-021-00456-4
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发表时间：
2021-01
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影响因子：
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通讯作者：
Shuhei Ikemoto;Kazuma Takahara;Taiki Kumi;K. Hosoda
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文献类型：
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作者：
Shuhei Ikemoto;Kazuma Takahara;Taiki Kumi;K. Hosoda
神经网络已广泛用于对难以制定的非线性系统进行建模。到目前为止，由于神经网络是一种完全不同的数学建模方法，因此控制理论尚未应用于它们，即使它们近似于控制对象的非线性状态方程。在这项研究中，我们提出了一种新方法，即神经模型提取，可以对针对非线性状态方程进行训练的前馈神经网络进行基于模型的控制。具体来说，我们提出了一种提取线性状态方程的方法，该方程相当于与给定输入向量对应的神经网络。我们对二自由度平面机械臂进行了简单的仿真，以验证所提出的方法如何实现对神经网络正向模型的基于模型的控制。通过模拟，假设机械手状态观测的不同设置，我们成功地证实了所提出方法的有效性。
Neural networks have been widely used to model nonlinear systems that are difficult to formulate. Thus far, because neural networks are a radically different approach to mathematical modeling, control theory has not been applied to them, even if they approximate the nonlinear state equation of a control object. In this research, we propose a new approach—i.e., neural model extraction, that enables model-based control for a feed-forward neural network trained for a nonlinear state equation. Specifically, we propose a method for extracting the linear state equations that are equivalent to the neural network corresponding to given input vectors. We conducted simple simulations of a two degrees-of-freedom planar manipulator to verify how the proposed method enables model-based control on neural network forward models. Through simulations, where different settings of the manipulator’s state observation are assumed, we successfully confirm the validity of the proposed method.