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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.

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