End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data
PositiveArtificial Intelligence
A new end-to-end algorithm has been developed for indirect data-driven control of bilinear systems, ensuring stability even in the presence of probabilistic noise. This advancement is significant as it leverages statistical learning theory to provide finite sample identification error bounds, making it a promising solution for complex control systems. The implications of this research could enhance the reliability and efficiency of various applications in engineering and technology.
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