Neural Networks for Control





Neural Networks for Control highlights key issues in learning control and identifies research directions that could lead to practical solutions for control problems in critical application domains. It addresses general issues of neural network based control and neural network learning with regard to specific problems of motion planning and control in robotics, and takes up application domains well suited to the capabilities of neural network controllers. The appendix describes seven benchmark control problems.

ContributorsAndrew G. Barto, Ronald J. Williams, Paul J. Werbos, Kumpati S. Narendra, L. Gordon Kraft, III, David P. Campagna, Mitsuo Kawato, Bartlett W. Met, Christopher G. Atkeson, David J. Reinkensmeyer, Derrick Nguyen, Bernard Widrow, James C. Houk, Satinder P. Singh, Charles Fisher, Judy A. Franklin, Oliver G. Selfridge, Arthur C. Sanderson, Lyle H. Ungar, Charles C. Jorgensen, C. Schley, Martin Herman, James S. Albus, Tsai-Hong Hong, Charles W. Anderson, W. Thomas Miller, III


Out of Print ISBN: 9780262132619 544 pp. | 6 in x 9 in


Out of Print ISBN: 9780262631617 544 pp. | 6 in x 9 in


W. Thomas Miller, III

W. Thomas Miller, III is Professor of Electrical and Computer Engineering at the University of New Hampshire.

Richard S. Sutton

Richard S. Sutton is Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta, and also Distinguished Research Scientist at DeepMind.

Paul J. Werbos

Paul J. Werbos is Program Director for Neuroengineering at the National Science Foundation.


  • A useful book for students and researchers alike, from any discipline that is interested in ANN-based controllers.

    Judy A. Franklin

    IEEE Transactions on Neural Networks

  • Highly recommended.

    Nick Beard