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Control-Oriented Neural Network for Systems with Nonlinear Elements
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DOI：10.1002/tee.70306.png)
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Abstract
En 中文
This paper proposes a novel neural network (NN) architecture for the unified representation of typical nonlinear elements in control systems, including dead zones, input saturation, and hysteresis. The proposed architecture employs a compact structure composed of rectified linear units, enabling enhanced learning efficiency and reduced computational complexity in comparison with conventional general-purpose recurrent neural networks. It is further demonstrated that the inverse models corresponding to these nonlinear elements can be formulated in a closed-form expression without approximation, thereby facilitating precise nonlinear compensation even in data-driven control frameworks. The effectiveness of the proposed NN is confirmed through several numerical examples.
Keywords:
nonlinear element
neural network
rectified linear unit
data-driven control
Journal
IF:
1.1
Papers:
215
Citations:
1.8K