Robust Adaptive Control via Neural Linearization and Compensation | Zendy

Roberto C. Laredo Rodríguez | Zendy; Wen Yu | Zendy

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Robust Adaptive Control via Neural Linearization and Compensation

Author(s) -

Roberto C. Laredo Rodríguez,

Wen Yu

Publication year - 2012

Publication title -

journal of control science and engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.208

H-Index - 18

eISSN - 1687-5257

pISSN - 1687-5249

DOI - 10.1155/2012/867178

Subject(s) - control theory (sociology) , artificial neural network , identifier , computer science , nonlinear system , compensation (psychology) , linearization , controller (irrigation) , adaptive control , feedback linearization , stability (learning theory) , projection (relational algebra) , control engineering , control (management) , engineering , artificial intelligence , algorithm , machine learning , psychology , physics , quantum mechanics , psychoanalysis , agronomy , biology , programming language

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

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