Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy

Area of Science:

• Control Engineering
• Artificial Intelligence
• Nonlinear System Analysis

Background:

• Neural network (NN) control for uncertain nonaffine systems faces challenges with NN approximation preconditions and fixed structures.
• Existing methods often struggle to adapt NN complexity dynamically, impacting performance and efficiency.

Purpose of the Study:

• To develop a new neural adaptive control approach for uncertain nonaffine systems.
• To address limitations in NN approximation and enable dynamic NN structure adjustment.
• To enhance the performance and learning capabilities of neural control systems.

Main Methods:

• Integration of locally weighted learning with barrier Lyapunov functions (BLF).
• Utilizing BLF to ensure bounded NN inputs during system operation.
• Implementing a neuron self-growing strategy for dynamic NN structure adaptation.

Main Results:

• The proposed method systematically addresses compact set preconditions for NN approximation.
• A self-adjustable NN structure is achieved, improving learning capabilities.
• Finite number of neurons are required, potentially reducing complexity compared to traditional methods.
• Continuous and smooth control actions are demonstrated, with minor exceptions during neuron addition.

Conclusions:

• The novel approach offers an effective solution for neural adaptive control of uncertain nonaffine systems.
• The dynamic NN structure and BLF integration lead to improved control performance and robustness.
• The method provides a more efficient and adaptable alternative to existing NN control techniques.