Proceedings of the 53rd Annual Meeting of the ISSS - 2009, Brisbane, Australia, Proceedings of the 53rd Annual Meeting of the International Society for the Systems Sciences

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Design of Fuzzy Neural Network Based Multi-Variables Controller for Manipulators

Yoshishige Sato


This paper proposes a robust multivariable control design by intelligent control that uses a fuzzy neural network by producing robustness capable of automatically controlling gain against a conventional, fixed PID control system. This structural feature of the proposed controller forms a nonlinear deviation compensator using fuzzy neural networks. Therefore, in multidimensionality the inverse dynamic model portion of the control law is referred to as a linearizing and decoupling control law. This method uses a control law where parameter response leads to critical damping and adaptive changes in gain according to time, making it possible to decouple mutual interference in each multivariable system.

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