Robust Adaptive Control via Neural Linearization and Compensation. (8th April 2012) - Rodríguez, Roberto Carmona; Yu, Wen
Abstract : 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.