Abstract
The concept of approximate dynamic programming and adaptive critic neural network based optimal controller is extended in this study to include systems governed by partial differential equations. An optimal controller is synthesized for a dispersion type tubular chemical reactor, which is governed by two coupled nonlinear partial differential equations. It consists of three steps: First, empirical basis functions are designed using the "Proper Orthogonal Decomposition" technique and a low-order lumped parameter system to represent the infinite-dimensional system is obtained by carrying out a Galerkin projection. Second, approximate dynamic programming technique is applied in a discrete time framework, followed by the use of a dual neural network structure called adaptive critics, to obtain optimal neurocontrollers for this system. In this structure, one set of neural networks captures the relationship between the state variables and the control, whereas the other set captures the relationship between the state and the costate variables. Third, the lumped parameter control is then mapped back to the spatial dimension using the same basis functions to result in a feedback control. Numerical results are presented that illustrate the potential of this approach. It should be noted that the procedure presented in this study can be used in synthesizing optimal controllers for a fairly general class of nonlinear distributed parameter systems.
Recommended Citation
R. Padhi and S. N. Balakrishnan, "Approximate Dynamic Programming Based Optimal Neurocontrol Synthesis of a Chemical Reactor Process Using Proper Orthogonal Decomposition," Proceedings of the International Joint Conference on Neural Networks, 2003, Institute of Electrical and Electronics Engineers (IEEE), Jan 2003.
The definitive version is available at https://doi.org/10.1109/IJCNN.2003.1223696
Meeting Name
International Joint Conference on Neural Networks, 2003
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
Galerkin Method; Galerkin Projection; Adaptive Critic Neural Network; Approximate Dynamic Programming; Chemical Reactors; Closed Loop Systems; Control System Synthesis; Dual Neural Network Structure; Dynamic Programming; Feedback Control; Low Order Lumped Parameter System; Lumped Parameter Control; Lumped Parameter Networks; Neurocontrollers; Nonlinear Differential Equations; Nonlinear Partial Differential Equations; Optimal Control; Optimal Neurocontrollers; Orthogonal Decomposition; Partial Differential Equations; Process Control; Tubular Chemical Reactor
International Standard Serial Number (ISSN)
1098-7576
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2003 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 2003
