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Article
Non-Linear System Control using Path Generating Neural Networks
Intelligent Engineering Systems through Artificial Neural Networks
  • Robert S. Woodley, Missouri University of Science and Technology
  • Levent Acar, Missouri University of Science and Technology
Abstract

An increasing amount of literature is available on the control of non-linear systems. For many of these systems, linear design techniques are ineffective. However, a non-linear augmentation of the linear control is sometimes able to perform the control. Determining the augmenting signal is a formidable task. However, recent work in the area of path planning has given rise to some useful tools. These tools can be used to design a complete controller. The final design takes advantage of the mapping abilities of neural networks to perform path planning. A path planning algorithm, taken from previously published work, was used to generate training data for the neural network. The results of simulation tests show that the controller composed of a linear gain and a path planning neural network was capable of controlling the non-linear system. The results are for a small class of non-trivial trajectories. Further work is planned to generalize the results.

Meeting Name
Artificial Neural Networks in Engineering Conference, ANNIE '98 (1998: Nov. 1-4, St. Louis, MO)
Department(s)
Electrical and Computer Engineering
Keywords and Phrases
  • Algorithms,
  • Computer simulation,
  • Light trailers,
  • Motion planning,
  • Neural networks,
  • Backward maneuvering,
  • Trailer truck,
  • Nonlinear control systems
International Standard Book Number (ISBN)
978-0791800829
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 1998 American Society of Mechanical Engineers (ASME), All rights reserved.
Publication Date
11-1-1998
Publication Date
01 Nov 1998
Citation Information
Robert S. Woodley and Levent Acar. "Non-Linear System Control using Path Generating Neural Networks" Intelligent Engineering Systems through Artificial Neural Networks Vol. 8 (1998) p. 599 - 604
Available at: http://works.bepress.com/levent-acar/25/