Back to Search
Start Over
Non-Iterative Data-Driven Tuning of Model-Free Control Based on an Ultra-Local Model
- Source :
- IEEE Access. 10:72773-72784
- Publication Year :
- 2022
- Publisher :
- IEEE (Institute of Electrical and Electronics Engineers), 2022.
-
Abstract
- In this paper, we present a data-driven tuning method for model-free control based on an ultra-local model (MFC-ULM), which is also called intelligent proportional-integral-derivative control. In industries, the control design must be easy, and it is important that the control law can be applied to nonlinear systems. The MFC-ULM has most of these features. However, in practice, trial-and-error tuning of MFC-ULM design parameters is necessary. To address this problem, we adopt a data-driven tuning approach. In the proposed method, the MFC-ULM design parameters can be tuned from single-experiment data without requiring system identification, and optimal parameters for the MFC-ULM are obtained using the least-squares method. Additionally, we adopt L-2-norm regularization to avoid overlearning. The effectiveness of this method was examined using simulations of two nonlinear systems. The results revealed that the MFC-ULM design parameters can be obtained directly without knowing the characteristics of the controlled object.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....0ba1363bb7e57e2143aeea6b885d854b