Title :
A neural network-based tracking control system
Author :
Tai, Heng-Ming ; Wang, Junli ; Ashenayi, Kaveh
Author_Institution :
Dept. of Electr. Eng., Tulsa Univ., OK, USA
fDate :
12/1/1992 12:00:00 AM
Abstract :
An application of the backpropagation neural network to the tracking control of industrial drive systems is presented. The merits of the approach lie in the simplicity of the scheme and its practicality for real-time control. Feedback error trajectories, rather than desired and/or actual trajectories, are employed as inputs to the neural network tracking controller. It can follow any arbitrarily prescribed trajectory even when the desired trajectory is changed to that not used in the training. Simulation was performed to demonstrate the feasibility and effectiveness of the proposed scheme
Keywords :
industrial control; neural nets; position control; tracking; backpropagation neural network; feedback error trajectories; industrial drive systems; neural network-based tracking control system; real-time control; Adaptive control; Artificial neural networks; Biological neural networks; Control systems; Electrical equipment industry; Industrial control; Neural networks; Sliding mode control; Trajectory; Uncertainty;
Journal_Title :
Industrial Electronics, IEEE Transactions on
