Synthesis of optimal feedback controller by neural networks
作者:
C. J. GOH,
N. J. EDWARDS,
期刊:
International Journal of Systems Science
(Taylor Available online 1994)
卷期:
Volume 25,
issue 8
页码: 1235-1248
ISSN:0020-7721
年代: 1994
DOI:10.1080/00207729408949275
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
In nonlinear optimal control problems, open-loop solutions from a fixed initial condition are much easier to compute than closed-loop solutions which do not depend on initial conditions. Two methods of using neural networks to approximate the optimal feedback controller are discussed. The indirect method uses a neural network to interpolate the whole field of extremals obtained from open-loop calculation. The direct method directly trains a neural network such that a general nonlinear optimal control performance index is minimized. The novelty of the modified backpropagation training is the requirement of the jacobian matrix of the neural network function. Simulation studies show that the closed-loop solution can be made to be arbitrarily close to the optimal open-loop solution with initial conditions chosen from a nontrivial subset of the state space.
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