Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances

Abstract

This paper proposes a control strategy consisting of a robust controller and an Echo State Network (ESN) based control law for stabilizing a class of uncertain nonlinear discrete-time systems subject to persistent disturbances. Firstly, the robust controller is designed to ensure that the closed-loop system is Input-to-State Stable (ISS) with a guaranteed stability region regardless of the ESN control action and exogenous disturbances. Then, the ESN based controller is trained in order to mitigate the effects of disturbances on the system output. A numerical example demonstrates the potentials of the proposed control design method.

References in corpus (4)

• Training robust neural networks using Lipschitz bounds
• Online learning-based Model Predictive Control with Gaussian Process Models and Stability Guarantees
• Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances
• Validation of Neural Network Controllers for Uncertain Systems Through Keep-Close Approach: Robustness Analysis and Safety Verification

Cited by in corpus (1)

• Combining Robust Control and Machine Learning for Uncertain Nonlinear Systems Subject to Persistent Disturbances