Inverse fuzzy model control : a data-driven learning framework with application to thermal process control

Škrjanc, Igor (author), Leite, Daniel (author), Gomide, Fernando (author), Tomažič, Simon (author)

Inverse fuzzy model control (IFMC) is an attractive strategy for regulating nonlinear thermal processes, where complex heat-transfer dynamics, disturbances, and actuator constraints challenge conventional control approaches. However, traditional direct and indirect inverse schemes often exhibit limited generalization and sensitivity to noise. This paper presents a data-driven inverse fuzzy control framework for temperature regulation in thermal systems, combining Takagi–Sugeno modeling, functional cancelation feedback, and auxiliary-input optimization. Gaussian antecedents and affine consequents are jointly identified via nonlinear least squares from input–output data generated by the theoretical PHE model. The same theoretical model is used as the numerical plant in all closed-loop simulations, whereas the forward and inverse fuzzy models are constructed only from the generated data and do not use the analytical PHE equations during identification or online inference. The learned inverse model estimates control actions that achieve the desired temperature trajectory while respecting actuator constraints. The method is evaluated in simulation on a plate heat exchanger (PHE) benchmark under tracking and disturbance-rejection scenarios. Results show accurate temperature regulation, smooth actuator behavior, and disturbance rejection in the tested scenarios. Compared with a numerically tuned PI baseline, the proposed approach reduces tracking RMSE by 27.5% and the standard deviation of the primary control current by 9.6% in the reported simulation scenario, indicating improved tracking and smoother primary actuation. In general, the proposed framework provides an interpretable and efficient solution for nonlinear thermal processes, with potential applications in energy systems, heat exchangers, and related thermal engineering technologies.

• Language: English
• Keywords: data-driven control, temperature control, Takagi– Sugeno systems, nonlinear system identification, inverse fuzzy control, thermal process control
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: 23 str.
• Numbering: Vol. 10, issue 8, art. 95
• PID: 20.500.12556/RUL-185352
• UDC: 681.5
• ISSN on article: 2305-7084
• DOI: 10.3390/chemengineering10080095
• COBISS.SI-ID: 286555139
• Publication date in RUL: 31.07.2026

Record is a part of a journal

• Title: ChemEngineering
• Shortened title: ChemEngineering
• Publisher: MDPI AG
• ISSN: 2305-7084
• COBISS.SI-ID: 529682457

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: vodenje na podlagi podatkov, regulacija temperature, sistemi Takagi–Sugeno, identfikacija nelinearnih sistemov, inverzno mehko vodenje, vodenje toplotnih procesov