Model-Free Control of Dynamical Systems with Deep Reservoir Computing
| Autor: | Andrew Pomerance, Daniel Canaday, Daniel J. Gauthier |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Dynamical systems theory Artificial neural network Computer Networks and Communications Computer science Complex system System identification Reservoir computing Process (computing) Systems and Control (eess.SY) Nonlinear control Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Computer Science Applications Artificial Intelligence Control theory FOS: Electrical engineering electronic engineering information engineering Information Systems |
| Popis: | We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|