Training Recurrent Neural Networks With the Levenberg-Marquardt Algorithm for Optimal Control of a Grid-Connected Converter

This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumula...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 26; no. 9; pp. 1900 - 1912
Main Authors: Fu, Xingang, Li, Shuhui, Fairbank, Michael, Wunsch, Donald C., Alonso, Eduardo
Format: Journal Article
Language:English
Published: United States IEEE 01.09.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Backpropagation through time (BPTT)
Computer Systems
d-q vector control
dynamic programming (DP)
forward accumulation through time (FATT)
grid-connected converter (GCC)
Humans
Jacobian matrix
Learning
Levenberg-Marquardt (LM)
Neural networks
Neural Networks (Computer)
Optimal control
Optimization
Pattern Recognition, Automated - methods
recurrent neural network (RNN)
Recurrent neural networks
Time Factors
Training
Trajectory
Vectors
Voltage control
recurrent neural network (RNN)
Backpropagation through time (BPTT)
dynamic programming (DP)
Jacobian matrix
forward accumulation through time (FATT)
optimal control
grid-connected converter (GCC)
d – q vector control
Levenberg-Marquardt (LM)
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:This paper investigates how to train a recurrent neural network (RNN) using the Levenberg-Marquardt (LM) algorithm as well as how to implement optimal control of a grid-connected converter (GCC) using an RNN. To successfully and efficiently train an RNN using the LM algorithm, a new forward accumulation through time (FATT) algorithm is proposed to calculate the Jacobian matrix required by the LM algorithm. This paper explores how to incorporate FATT into the LM algorithm. The results show that the combination of the LM and FATT algorithms trains RNNs better than the conventional backpropagation through time algorithm. This paper presents an analytical study on the optimal control of GCCs, including theoretically ideal optimal and suboptimal controllers. To overcome the inapplicability of the optimal GCC controller under practical conditions, a new RNN controller with an improved input structure is proposed to approximate the ideal optimal controller. The performance of an ideal optimal controller and a well-trained RNN controller was compared in close to real-life power converter switching environments, demonstrating that the proposed RNN controller can achieve close to ideal optimal control performance even under low sampling rate conditions. The excellent performance of the proposed RNN controller under challenging and distorted system conditions further indicates the feasibility of using an RNN to approximate optimal control in practical applications.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2014.2361267