Nonlinear fractional optimal control problems with neural network and dynamic optimization schemes

Nonlinear fractional optimal control problems with neural network and dynamic optimization schemes This paper deals with a numerical technique for fractional optimal control problems (FOCPs) based on a neural network scheme. The fractional derivative in these problems is in the Riemann–Liouville sense. The fractional derivative is approximated using the Grunwald–Letnikov definition for numerical computation. According to the Pontryagin’s minimum principle (PMP) for FOCPs and by constructing an error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for the state, costate and control functions where these trial solutions are constructed by using two-layered perceptron. We then minimize the error function where weights and biases associated with all neurons are unknown. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Illustrative examples are included to demonstrate the validity and capability of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Nonlinear Dynamics Springer Journals

Nonlinear fractional optimal control problems with neural network and dynamic optimization schemes

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Publisher
Springer Netherlands
Copyright
Copyright © 2017 by Springer Science+Business Media B.V.
Subject
Engineering; Vibration, Dynamical Systems, Control; Classical Mechanics; Mechanical Engineering; Automotive Engineering
ISSN
0924-090X
eISSN
1573-269X
D.O.I.
10.1007/s11071-017-3616-9
Publisher site
See Article on Publisher Site

Abstract

This paper deals with a numerical technique for fractional optimal control problems (FOCPs) based on a neural network scheme. The fractional derivative in these problems is in the Riemann–Liouville sense. The fractional derivative is approximated using the Grunwald–Letnikov definition for numerical computation. According to the Pontryagin’s minimum principle (PMP) for FOCPs and by constructing an error function, we define an unconstrained minimization problem. In the optimization problem, we use trial solutions for the state, costate and control functions where these trial solutions are constructed by using two-layered perceptron. We then minimize the error function where weights and biases associated with all neurons are unknown. Substituting the optimal values of the weights and biases in the trial solutions, we obtain the optimal solution of the original problem. Illustrative examples are included to demonstrate the validity and capability of the proposed method.

Journal

Nonlinear DynamicsSpringer Journals

Published: Jun 24, 2017

References

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