Adaptive-Critic Based Optimal Neuro Control Synthesis for Distributed Parameter Systems
Abstract
A neural network based optimal control synthesis approach is presented for systems modeled by partial differential equations. The problem is formulated via discrete dynamic programming and the necessary conditions of optimality are derived. For synthesis of the controller, we propose two sets of neural networks: the set of action networks captures the mapping between the state and control, while the set of critic networks captures the mapping between the state and costate. We illustrate the solution process with a parabolic equation involving a nonlinear term. For comparison, we consider the linear quadratic regulator problem for the diffusion equation, for which the Ricatti-operator based solution is known. Results show that this adaptive-critic based systematic approach holds promise for obtaining the optimal control design of both linear and nonlinear distributed parameter systems. © 2001 Elsevier Science Ltd.
Recommended Citation
R. Padhi et al., "Adaptive-Critic Based Optimal Neuro Control Synthesis for Distributed Parameter Systems," Automatica, International Federation of Automatic Control, Jan 2001.
The definitive version is available at https://doi.org/10.1016/S0005-1098(01)00093-0
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
0005-1098
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2001 International Federation of Automatic Control, All rights reserved.
Publication Date
01 Jan 2001
