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• W2948353014 abstract "The objective of this letter is to analyze the stability properties, the training procedure and the use in predictive control schemes of echo state networks (ESNs), a specific class of recurrent neural networks. First, a sufficient condition guaranteeing incremental input-to-state stability (δ ISS) of ESNs is derived. Then, an automatic procedure to optimally tune the parameters of the ESN in the training phase is presented, which allows to enforce δ ISS. Finally, the application of the ESN as a model of the plant for predictive control purposes is studied. In particular, an asymptotically convergent observer is designed, and a model predictive controller with guaranteed stabilizing properties is devised for the solution to regulation problems. Simulation results on a nonlinear process for pH neutralization confirm the effectiveness of the proposed control scheme." @default.
• W2948353014 created "2019-06-14" @default.
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• W2948353014 date "2019-10-01" @default.
• W2948353014 modified "2023-09-25" @default.
• W2948353014 title "Model Predictive Control Design for Dynamical Systems Learned by Echo State Networks" @default.
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• W2948353014 doi "https://doi.org/10.1109/lcsys.2019.2920720" @default.
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