Model-Free Control of Dynamical Systems with Deep Reservoir Computing
This provides a simpler and more efficient control solution for complex systems like chaotic dynamics, though it is incremental as it builds on existing reservoir computing techniques.
The authors tackled the problem of controlling unknown, complex dynamical systems by proposing a model-free control method using deep reservoir computing, which achieved precise control of chaotic systems to target trajectories without prior knowledge or system identification.
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.