Fast Nonlinear Model Predictive Control Using Long Short-Term Memory Networks
- Title
- Fast Nonlinear Model Predictive Control Using Long Short-Term Memory Networks
- Authors
- Meyer, Alexander
- Date Issued
- 2023
- Publisher
- 포항공과대학교
- Abstract
- Nonlinear Model Predictive Control (NLMPC) is one of the most convenient
and optimal ways to control nonlinear systems but is plagued by issues with computational
efficiency. Most of the algorithm is quite fast, but solving an optimization
problem online at each time step causes NLMPC response times to be unpredictable
and too slow for many systems. To address this issue, this paper uses supervised learning
to learn the behavior of an NLMPC controller, replacing the entire control system
architecture with a single Long Short-Term Memory (LSTM) network. We discuss
the dataset structure and a procedure for generating it and show that the LSTM has
comparable performance to NLMPC when deployed to an inverted rotary pendulum
system. Computation time for the LSTM during inference is stable and several orders
of magnitude higher than that of the NLMPC controller, with typical controller
response times around 13 [ms].
- URI
- http://postech.dcollection.net/common/orgView/200000663052
https://oasis.postech.ac.kr/handle/2014.oak/118218
- Article Type
- Thesis
- Files in This Item:
- There are no files associated with this item.
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