Data-driven fuzzy control with experimental validation, 249844.58 EUR, national exploratory research grant (PCE), financed by the Executive Agency for Higher Education, Research, Development and Innovation Funding - UEFISCDI), 2021-2023, project code: PN-III-P4-ID-PCE-2020-0269
- Fuzzy controllers are an important part of the general class of nonlinear controllers as they are relatively easily understandable and also offer very good control system (CS) performance. An alternative to the classical model-based control is represented by data-driven control (DDC), a hot topic in academia and industry as well. The advantage of DDC is the use of only the input-output data of the process, which is useful if the mathematical model of the controlled process is complex or its identification is very difficult. That is the main reason of the high increased interest for nonlinear controllers whose parameters are determined using the input-output data of the controlled processes, especially in the area of industrial control engineering practitioners. This project proposes the development of new data-driven fuzzy controllers (DDFCs) for nonlinear processes with shape memory alloy (SMA) actuators in order to benefit from the advantages of both fuzzy control and DDC. SMA, as a relatively complex nonlinear process, is challenging and also advantageous by silent operation and acting by contractions like a human muscle, playing the role of actuator in CSs. The new CS structures with DDFCs will be tested and validated experimentally on several classes of processes that include SMA actuators and lab equipment of the research team. They will also be validated as industrial controllers with the support of team’s external partners from Romania, Canada, UK and Australia.
- The main objective of this project is to develop new data-driven fuzzy controllers for nonlinear processes. The achievement of this objective requires the achievement of the following particular objectives (1., 2., 3., 4., 5., 6. and 7.) during the three years of the project:
- 1. The analysis, design and implementation of new DDC algorithms.
- 2. The analysis, design and implementation of new fuzzy control (FC) algorithms.
- 3. The analysis, design and implementation of three new DDFC algorithms.
- 4. The validation of the new control algorithms by experiments conducted on laboratory equipment that may include SMA actuators.
- 5. The validation of the proposed control algorithms as controllers for real-world processes.
- 6. The dissemination of results focusing on high visibility journals and important conferencess.
- 7. Solving the project management issues.
- Minimum three papers in high impact leading journals.
- Six conference papers presented at visible international conferences.
- Three data-driven fuzzy controllers ready to implement in industry.
Overall results (2021-2023):
- 2 papers published in Clarivate Analytics Web of Science (formerly ISI Web of Knowledge) journals with impact factors, cumulated impact factor according to 2020 Journal Citation Reports (JCR) released by Clarivate Analytics in 2021 = 6.660.
- 0 papers published in conference proceedings and book chapters indexed in Clarivate Analytics Web of Science (formerly ISI Web of Knowledge or ISI Proceedings).
- 1 paper published in conference proceedings indexed in international databases (IEEE Xplore, INSPEC, Scopus, DBLP).
Results in 2021:
- 2 papers published in Clarivate Analytics Web of Science (formerly ISI Web of Knowledge) journals with impact factors, cumulated impact factor according to 2020 Journal Citation Reports (JCR) released by Thomson Reuters in 2021 = 6.660, 0 papers published in conference proceedings indexed in Clarivate Analytics Web of Science (formerly ISI Web of Knowledge or ISI Proceedings), 1 paper published in conference proceedings indexed in international databases (IEEE Xplore, INSPEC, Scopus, DBLP), 0 book chapters published in Springer volumes.
- R.-E. Precup, R.-C. David, R.-C. Roman, A.-I. Szedlak-Stînean and E. M. Petriu, Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using slime mould algorithm, International Journal of Systems Science (Taylor and Francis), DOI: 10.1080/00207721.2021.1927236, pp. 1-16, 2021, impact factor (IF) = 2.281, IF according to 2020 Journal Citation Reports (JCR) released by Clarivate Analytics in 2021 = 2.281 (www.tandfonline.com).
- A. Topîrceanu and R.-E. Precup, A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics, Scientific Reports (Nature), vol. 11, paper 14341, pp. 1-12, 2021, impact factor (IF) = 4.379, IF according to 2020 Journal Citation Reports (JCR) released by Clarivate Analytics in 2021 = 4.379 (www.nature.com).
- C.-V. Pop, R.-E. Precup and L. I. Cădariu-Brăiloiu, Analysis of Monetary Policy Decisions of the National Bank of Romania with Text Mining Techniques, Proceedings of 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics SACI 2021, Timisoara, Romania, pp. 21-26, 2021, indexed in IEEE Xplore, DBLP (ieeexplore.ieee.org).