20.500.12556/RUL-185352 Inverse fuzzy model control a data-driven learning framework with application to thermal process control 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. data-driven control temperature control Takagi– Sugeno systems nonlinear system identification inverse fuzzy control thermal process control vodenje na podlagi podatkov regulacija temperature sistemi Takagi–Sugeno identfikacija nelinearnih sistemov inverzno mehko vodenje vodenje toplotnih procesov true false true Angleški jezik Slovenski jezik Članek v reviji 2026-07-31 10:31:56 2026-07-31 10:32:04 2026-08-01 04:04:44 0000-00-00 00:00:00 2026 0 0 23 str. issue 8, art. 95 Vol. 10 Avg. 2026 0000-00-00 Zaloznikova Objavljeno apc 0000-00-00 0000-00-00 0000-00-00 681.5 2305-7084 10.3390/chemengineering10080095 286555139 ChemEngineering-10-00095-v2.pdf ChemEngineering-10-00095-v2.pdf 1 4EBA011E113C1F2FCA200540DACBB2D4 97c3767d0b7965dfe4187d473235b0458278ae29a85b248e36c634c87dc6437c 57f12209-8cba-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=243805 https://www.mdpi.com/2305-7084/10/8/95 1 03ff2d94-8cba-11f1-9b0d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=243803 Fakulteta za elektrotehniko 0 0 0