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Adaptive Neural Network Controllers for Dynamics Systems
Takayuki Yamada and Tetsuro Yabuta
NTT Transmission Systems Laboratories Tokai, Ibaraki, 319-11, Japan
Published:August 20, 1990
Abstract
Many studies such as Kawato's work have been undertaken in order to apply both the flexibility and learning ability of neural networks to dynamic system controllers. However, their characteristics have not yet been completely clarified. On the other hand, many studies have established conventional control theories such as adaptive control. If we can clarify the relationship between neural network controllers and adaptive controllers, the two control algorithms will be developed considerably by making use of the advantages of each. Therefore, this paper proposes a neural network direct controller in order to construct an interface between neural network and conventional control theories. This paper also proposes an open loop type of controller in order to realize inverse dynamics using only the neural network. Analytical approaches prove the local stability of the proposed controllers. Simulated and experimental results verify their realization and confirm their characteristics. This paper also discusses the relationship between neural network controllers and adaptive controllers.
Cite this article as:T. Yamada and T. Yabuta, “Adaptive Neural Network Controllers for Dynamics Systems,” J. Robot. Mechatron., Vol.2 No.4, pp. 245-257, 1990.Data files:
