Echo State Networks for Practical Nonlinear Model Predictive Control of Unknown Dynamic Systems

Nonlinear model predictive control (NMPC) of industrial processes is changeling in part because the model of the plant may not be completely known but also for being computationally demanding. This work proposes an extremely efficient reservoir computing (RC)-based control framework that speeds up t...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2022-06, Vol.33 (6), p.2615-2629
Hauptverfasser:	Jordanou, Jean Panaioti, Antonelo, Eric Aislan, Camponogara, Eduardo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analytical models Computational modeling Computer architecture Control algorithms Dynamical systems Echo state networks (ESNs) Linear control Long short-term memory model predictive control (MPC) Nonlinear control Nonlinear dynamical systems Nonlinear systems Predictive control Predictive models reservoir computing (RC) Reservoirs Sensitivity Training
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