Robust Adaptive Control via Neural Linearization and Compensation

Abstract EN

We propose a new type of neural adaptive control via dynamic neural networks.

For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used.

Dead-zone and projection techniques are applied to assure the stability of neural identification.

Then four types of compensator are addressed.

The stability of closed-loop system is also proven.