Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 26, Issue 9, Pages 1900-1912Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2014.2361267
Keywords
Backpropagation through time (BPTT); d-q vector control; dynamic programming (DP); forward accumulation through time (FATT); grid-connected converter (GCC); Jacobian matrix; Levenberg Marquardt (LM); optimal control; recurrent neural network (RNN)
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Funding
- U.S. National Science Foundation [EECS-1102038, 1102159]
- Mary K. Finley Missouri Endowment
- Missouri Science and Technology Center for Infrastructure Engineering Studies and Intelligent Systems Center
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1102159] Funding Source: National Science Foundation
- Div Of Industrial Innovation & Partnersh
- Directorate For Engineering [1414379] Funding Source: National Science Foundation
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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 gridconnected 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 reallife 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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