Title

Controller/Critic Neurocontrol Design Method Based on Neighboring Optimal Control

Editor(s)

Dagli, Cihan H., 1949- and Akay, M. F. and Chen, C. L. P. and Fernandez, B.R. and Ghosh, J.

Abstract

The reinforcement learning approach to intelligent control decomposes the control task into controller and critic subtasks. The controller provides control actions based on observed plant states and the critic assesses control actions. The critic assessment may then be used to adjust controller structure to improve long-term control system performance. We demonstrate a design method using optimal control theory for controller design and neighboring optimal control for critic design. The resulting control system is capable of robust, near optimal control of nonlinear, time-varying plants.

Meeting Name

Intelligent Engineering Systems Through Artificial Neural Networks (1995, St.Louis, MO, USA)

Department(s)

Mechanical and Aerospace Engineering

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1995 American Society of Mechanical Engineers (ASME), All rights reserved.

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