Control of chaotic systems via reservoir computing approach

We propose a data-driven approach to realize chaotic control. By virtue of the reservoir computing approach, we obtain an appealing model for characterizing chaotic systems with only observational data required. By applying the Grebogi-Yorke algorithm to the reservoir computing model, we show that the dynamical variables for characterizing trajectory evolution indicate successful synchronization in the considered systems. We sample data from several chaotic systems as well as real-world systems to demonstrate the effectiveness of our approach. Our work overcomes the reliance of traditional chaos control on analytical system models, thereby extending chaotic control theory to more complex industrial scenarios.