Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems

Area of Science:

• Control Systems Engineering
• Machine Learning
• Nonlinear Dynamics

Background:

• Adaptive Neural Control (ANC) is crucial for complex nonlinear systems.
• Existing methods struggle with strict-feedback systems and unknown affine terms.
• Learning in control requires robust stability guarantees and efficient training.

Purpose of the Study:

• To develop a novel learning-based adaptive neural control method for nonlinear strict-feedback systems.
• To ensure system stability and finite-time convergence of tracking errors.
• To enable efficient learning and reuse of system dynamics knowledge.

Main Methods:

• Input-to-State Stability (ISS) modular ANC design for guaranteed boundedness.
• Decomposition of the closed-loop system into linear time-varying (LTV) perturbed subsystems.
• Recursive design utilizing radial basis function neural networks (NNs) and partial persistent excitation.

Main Results:

• The proposed ISS-modular ANC ensures closed-loop signal boundedness and finite-time tracking error convergence.
• Learning is achieved through accurate approximation of system dynamics via NNs.
• Exponential stability of LTV subsystems is guaranteed, enabling effective learning.

Conclusions:

• The developed method successfully implements learning in adaptive neural control for challenging nonlinear systems.
• Learned knowledge is effectively reused, enhancing stability and performance while reducing training overhead.
• Simulation results validate the proposed approach's effectiveness.