Adaptive PID control using TSK elliptic fuzzy and static feedforward for observable disturbance rejection in industrial heating ovens
Abstract
This paper presents an advanced control strategy aimed at accelerating the temperature recovery time in industrial heating ovens, particularly in response to observable disturbances such as periodic door openings. The proposed method combines a Takagi-Sugeno-Kang (TSK) elliptic fuzzy-based adaptive proportional-integral-derivative (PID) control with a static feedforward (FF) control strategy. The TSK elliptic fuzzy system models the input-output dynamics and adaptively adjusts the PID gains, allowing the controller to respond effectively to varying system conditions. The static feedforward control is designed to specifically counteract the measurable disturbances to shorten recovery time and improve stability. The strategy is validated through both simulation and experiment on a low-cost STM32 microcontroller. Compared with a conventional PID controller, it reduced the RMSE by 32.27 % and the ISE by 54.13 %, together with a recovery time shortened by approximately 72 s, reflecting the faster disturbance recovery achieved by the static feedforward action. Experimental results confirmed these findings, with a higher feedforward gain further reducing the temperature drop and accelerating recovery. The proposed technique offers a reliable and practical solution for practitioners in managing similar disturbance patterns in industrial settings.
Keywords
Full Text:
PDFReferences
N. Wang et al., “High efficiency thermoelectrictemperature control system with improved proportionalintegral differential algorithm using energy feedback technique,” IEEE Trans. Ind. Electron., vol. 69, no. 5, pp. 5225–5234, May 2022.
A. Rospawan, C. C. Tsai, and C. C. Hung, “Output recurrent fuzzy broad learning systems for adaptive MIMO PID control: theory, simulations, and application,” IEEE Access, vol. 12, pp. 19388–19404, 2024.
J. Hao, G. Zhang, W. Liu, Y. Zheng, and L. Ren, “Datadriven tracking control based on LM and PID neural network with relay feedback for discrete nonlinear systems,” IEEE Transactions on Industrial Electronics, vol. 68, no. 11, pp. 11587–11597, Nov. 2021.
Y. Hou, “Adaptive virtual impedance control strategy based on IWOA-fuzzy PID and its application to reactive power sharing in islanded microgrids,” Scientific Reports, vol. 16, no. 1, p. 2833, Dec. 2025.
J. S. Saputro et al., “Design of intelligent cruise control system using fuzzy-PID control on autonomous electric vehicles prototypes,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 15, no. 1, pp. 105–116, Jul. 2024.
P. Kaliappan, A. Ilangovan, S. Muthusamy, and B. Sembanan, “Temperature control design with differential evolution based improved adaptive-fuzzyPID techniques,” Intelligent Automation & Soft Computing, vol. 36, no. 1, pp. 781–801, 2022.
M. Liu, L. Cheng, J. Xu, X. Zhang, and H. Zhang, “An improved particle swarm fuzzy PID for adaptive control of temperature in CFRP induction heating,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 238, no. 4, pp. 500–513, Mar. 2024.
B. Ghosh and S. Mandal, “Enhanced solar PV cell parameter identification via particle swarm optimization (PSO) with weighted objective function,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 16, no. 1, pp. 15–26, Jun. 2025.
S. Asgari, A. A. Suratgar, and M. Kazemi, “Feedforward fractional order PID load frequency control of microgrid using harmony search algorithm,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 45, no. 4, pp. 1369–1381, Dec. 2021.
Z. Bingul and K. Gul, “Intelligent-PID with PD Feedforward Trajectory Tracking Control of an Autonomous Underwater Vehicle,” Machines, vol. 11, no. 2, Art. no. 2, Feb. 2023.
Z. Liu, H. Chen, L. Peng, X. Ye, S. Xu, and T. Zhang, “Feedforward-decoupled closed-loop fuzzy proportionintegral-derivative control of air supply system of proton exchange membrane fuel cell,” Energy, vol. 240, p. 122490, Feb. 2022.
H. Yuan, H. Dai, W. Wu, J. Xie, J. Shen, and X. Wei, “A fuzzy logic PI control with feedforward compensation for hydrogen pressure in vehicular fuel cell system,” International Journal of Hydrogen Energy, vol. 46, no. 7, pp. 5714–5728, Jan. 2021.
D. H. Pham, C. M. Lin, V. N. Giap, V. P. Vu, and H. Y. Cho, “Design of missile guidance law using TakagiSugeno-Kang (TSK) elliptic type-2 fuzzy brain imitated neural networks,” IEEE Access, vol. 11, pp. 53687–53702, 2023.
D.-H. Pham, C.-M. Lin, V.-N. Giap, V.-T. Nguyen, and N.-T. Pham, “Self-organizing Takagi–Sugeno–Kang fuzzy elliptic type-2 CMAC for nonlinear systems with uncertainty,” Int. J. Fuzzy Syst., May 2025.
A. Rospawan, C. C. Tsai, and C. C. Hung, “Two-layer intelligent learning control using output recurrent fuzzy neural long short-term memory broad learning system with RMSprop,” IEEE Access, vol. 13, pp. 34334–34349, Feb. 2025.
I. I. Novendra et al., “Optimization of load frequency control using grey wolf optimizer in micro hydro power plants,” Journal of Mechatronics, Electrical Power, and Vehicular Technology, vol. 14, no. 2, pp. 166–176, Dec. 2023.
A. Rospawan, C. C. Tsai, and C. C. Hung, “Intelligent MIMO ORFBLS-based setpoint tracking control with its application to temperature control of an industrial extrusion barrel,” International Journal of Fuzzy Systems, vol. 27, no. 3, pp. 774–790, Jun. 2024.
K. A. Metwally et al., “Drying kinetics, power consumption, economic and environmental analysis of pomegranate peels drying using a hybrid solar dryer compared with oven dryer,” Scientific Reports, vol. 16, no. 1, p. 7395, Feb. 2026.
J. Meng, W. Sun, Q. F. Pan, and M. X. Ruan, “Research and application of improved particle swarm fuzzy PID algorithm based on self- disturbance rejection in temperature control system of plastic extruder,” IEEE Access, vol. 12, pp. 41620–41630, 2024.
A. Rospawan et al., “Adaptive nonlinear PID control of DC motor position using a polynomial fuzzy long shortterm memory neural network,” International Journal of Automotive and Mechanical Engineering, vol. 22, no. 3, pp. 12821–12836, Oct. 2025.
T. Wang, R. Gault, and D. Greer, “Ensemble learningbased fuzzy aggregation functions and their application in TSK neural networks,” Int. J. Fuzzy Syst., vol. 27, no. 4, pp. 1115–1126, Jun. 2025.
A. Rospawan et al., “Direct control strategy using polynomial fuzzy-based adaptive fractional order PID controller,” Makara Journal of Technology, vol. 29, no. 2, Aug. 2025.
V.-T. Nguyen, D.-N. Duong, D.-H. Pham, V.-T. Ngo, and L. A. Tuan, “Optimal nonlinear PID TSK3DCMAC controller based on balancing composite motion optimization for ballbot with external forces,” ISA Transactions, vol. 158, pp. 654–673, Mar. 2025.
C. C. Tsai, C. C. Hung, C. F. Mao, H. S. Wu, and C. H. Chen, “Fuzzy neural LSTM-RBLS for fractional-order PID sliding-mode motion control of autonomous mobile robots with four ISID wheels,” Int. J. Fuzzy Syst., vol. 27, pp. 2195–2213, 2025.
N. Querejeta, S. García, N. Álvarez-Gutiérrez, F. Rubiera, and C. Pevida, “Measuring heat capacity of activated carbons for CO2 capture,” Journal of CO2 Utilization, vol. 33, pp. 148–156, Oct. 2019.
M. Ma, L. Qian, Y. Zhang, Q. Fang, and G. Xue, “An adaptive double-parameter softmin based TakagiSugeno-Kang fuzzy system for high-dimensional data,” Fuzzy Sets and Systems, vol. 521, p. 109582, Dec. 2025.
Article Metrics
Metrics powered by PLOS ALM
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Ali Rospawan, Clara Lavita Angelina, I Made Andik Setiawan, Zanu Saputra, Ocsirendi Ocsirendi, Aan Febriansyah, Indra Dwisaputra

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.




