Self-tuning control by neural networks

Author

Lee, Minho ; Lee, Soo-Young ; Park, Cheol Hoon

Author_Institution

Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Taejon, South Korea

Volume

4

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2411

Abstract

A new self-tuning controller consisting of a PD controller, an inverse dynamics compensator, and a neural controller is proposed. In order to train the neural controller located in front of a system, the inverse dynamics of the system is used to calculate the inverse Jacobian of the unknown system. With the neural identifier the overall control architecture can be made stable. The control performance is compared with that of a conventional controller without the neural networks. Computer simulation results show that the proposed control architecture is effective in controlling of a robotic system

Keywords

compensation; dynamics; neural nets; neurocontrollers; robots; self-adjusting systems; two-term control; PD controller; inverse Jacobian; inverse dynamics compensator; neural controller; neural networks; robotic system; self-tuning controller; Computer architecture; Computer simulation; Control systems; Error correction; Jacobian matrices; Multi-layer neural network; Neural networks; PD control; Robot control; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374597

Filename

374597