A Systematic Synthesis of Optimal Process Control with Neural Networks
Abstract
Infinite time optimal controllers have been designed for a dispersion type tubular reactor model in the framework of adaptive-critic based neuro-controller design. For the reactor control problem, which is governed by two coupled nonlinear partial differential equations, an optimal controller synthesis scheme is presented using two sets of neural networks. One set of neural networks captures the relationship between the states and the control, whereas the other set of networks captures the relationship between the states and the costates. This innovative approach solves the optimal controller in a feedback form. This methodology can be viewed as a practical computational tool in designing optimal controllers for distributed parameter systems, in general.
Recommended Citation
R. Padhi and S. N. Balakrishnan, "A Systematic Synthesis of Optimal Process Control with Neural Networks," Proceedings of the American Control Conference (2001, Arlington, VA), Institute of Electrical and Electronics Engineers (IEEE), Jan 2001.
The definitive version is available at https://doi.org/10.1109/ACC.2001.946018
Meeting Name
American Control Conference (2001, Arlington, VA)
Department(s)
Mechanical and Aerospace Engineering
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2001 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2001
