Title :
Trajectory tracking using neural networks
Author_Institution :
Dept. of Electr. Eng., Tulsa Univ., OK, USA
Abstract :
Presents a method for tracking prespecified trajectories in industrial drive systems with a multilayered feedforward neural network. The method utilizes the backpropagation technique to learn feedback-error ranges, in which appropriate control actions can be generated from a lookup table. It can follow arbitrarily prescribed trajectories even when they are not present in the training phase. This approach is simple and practical for real-time implementation. Examples are included to demonstrate the effectiveness. The analogy between this scheme and a fuzzy logic control strategy is also investigated
Keywords :
backpropagation; feedforward neural nets; fuzzy control; industrial robots; position control; backpropagation technique; fuzzy logic control; industrial drive systems; lookup table; multilayered feedforward neural network; prespecified trajectories; real-time implementation; training phase; Adaptive control; Backpropagation algorithms; Control systems; Electrical equipment industry; Error correction; Fuzzy logic; Industrial control; Neural networks; Signal generators; Trajectory;
Conference_Titel :
Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
0-7803-0593-0
DOI :
10.1109/ISCAS.1992.230637
