Adaptive control of dynamical systems using reservoir computing

Area of Science:

• Complex Systems
• Nonlinear Dynamics
• Machine Learning

Background:

• Dynamical systems often require adaptive control strategies to achieve desired states.
• Reservoir computing offers a powerful framework for processing time-series data from complex systems.

Purpose of the Study:

• To develop and demonstrate a data-driven adaptive control technique for dynamical systems.
• To leverage reservoir computing for predicting system parameters and generating control signals.
• To validate the control scheme's effectiveness across diverse system attractors and initial conditions.

Main Methods:

• Utilizing reservoir computing to train a model that predicts system parameters from time-series data.
• Developing a feedback control signal based on the predicted system parameters.
• Applying the control signal to guide the dynamical system towards a target state.
• Validating the approach through numerical simulations and implementation on a physical Rössler system circuit.

Main Results:

• The reservoir computing approach successfully predicts system parameters from time-series data.
• The developed control signal effectively drives dynamical systems to arbitrary target attractors.
• The method demonstrates robustness across various attractor types and initial conditions.
• Successful implementation on a Rössler system electronic circuit confirms practical applicability.

Conclusions:

• The proposed data-driven adaptive control method, powered by reservoir computing, offers an efficient and versatile approach for dynamical systems.
• The technique requires minimal training data, making it practical for real-world applications.
• This work paves the way for advanced control strategies in complex systems through machine learning.