Title :
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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374597
