Parallel Trajectory Training of Recurrent Neural Network Controllers with Levenberg–Marquardt and Forward Accumulation Through Time in Closed-loop Control Systems
Fu, X., Sturtz, J., Alonso, E. 
ORCID: 0000-0002-3306-695X , Qingge, L. & Challoo, R. (2023).
Parallel Trajectory Training of Recurrent Neural Network Controllers with Levenberg–Marquardt and Forward Accumulation Through Time in Closed-loop Control Systems.
IEEE Transactions on Sustainable Computing, 9(2),
pp. 222-229.
doi: 10.1109/tsusc.2023.3330573
Abstract
This paper introduces a novel parallel trajectory mechanism that combines Levenberg-Marquardt and Forward Accumulation Through Time algorithms to train a recurrent neural network controller in a closed-loop control system by distributing the calculation of trajectories across Central Processing Unit (CPU) cores/workers depending on the computing platforms, computing program languages, and software packages available. Without loss of generality, the recurrent neural network controller of a grid-connected converter for solar integration to a power system was selected as the benchmark test closed-loop control system. Two software packages were developed in Matlab and C++ to verify and demonstrate the efficiency of the proposed parallel training method. The training of the deep neural network controller was migrated from a single workstation to both cloud computing platforms and High-Performance Computing clusters. The training results show excellent speed-up performance, which significantly reduces the training time for a large number of trajectories with high sampling frequency, and further demonstrates the effectiveness and scalability of the proposed parallel mechanism.
| Publication Type: | Article |
|---|---|
| Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Publisher Keywords: | Parallel Trajectory Training, Recurrent Neural Network Controller, Forward Accumulation Through Time, Levenberg–Marquardt, Cloud Computing, High-Performance Computing (HPC) Cluster |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology > Computer Science |
| SWORD Depositor: |
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