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A Generative Verification Framework on Statistical Stability for Data-Driven Controllers
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DOI：10.1109/ACCESS.2023.3236917.png)
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Abstract
En 中文
This study proposes a novel framework for evaluating the stability of data-driven controllers and the concept of statistical stability. The proposed framework can be used when it is challenging to show stability through conventional control theory. The novelty of this paper lies in that it provides a method for scientifically analyzing the stability of data-driven controllers, thereby improving the quality of data-driven controllers. The proposed framework consists of three parts: the generative model, controller optimizer, and verification model. A variational autoencoder is used to classify and randomly generate data, and the generated data are used to train the controller. A support vector machine is used to classify areas where the controller is statistically stable. The statistical stability of an optimal controller designed using a deep neural network structure is analyzed using the proposed framework.
Keywords:
Neural networks
Deep learning
Control systems
Numerical stability
Stability criteria
Adaptive control
Lyapunov methods
Data-driven analysis
deep neural network
nonlinear controller
nonlinear system verification
optimal control
performance analysis
statistical stability
variational autoencoder