Restricted accessResearch articleFirst published online 2026
Hybrid linear and nonlinear active disturbance rejection control for a gas turbine power generation system using a lookup table-based type-2 fuzzy method
Selecting appropriate dynamics and developing accurate models for gas turbines is a complex yet essential task, as model accuracy directly affects the performance of model-based control strategies. To address this challenge, this article employs the Active Disturbance Rejection Control (ADRC) approach, a model-free control method, for the operation of gas turbines. In the proposed scheme, Linear ADRC (LADRC) is applied to regulate the fuel flow and exhaust temperature, while Nonlinear ADRC (NLADRC) is used for rotor speed control. Considering the inherent delays in gas turbine systems, a predictor observer is incorporated. Furthermore, the controller parameters are optimized using a type-2 fuzzy method, where a lookup table is implemented to accelerate the computation process compared to real-time fuzzy inferences significantly. Simulation results confirm the effectiveness of the proposed controller in handling uncertainties and external disturbances.
BonfiglioACacciacarneSInvernizziM, et al. (2017) Gas turbine generating units control via feedback linearization approach. Energy121: 491–512.
2.
BonfiglioACacciacarneSInvernizziM, et al. (2018) A sliding mode control approach for gas turbine power generators. IEEE Transactions on Energy Conversion34(2): 921–932.
3.
CastilloOMelinPKacprzykJ, et al. (2007) Type-2 fuzzy logic: Theory and applications. In: 2007 IEEE international conference on granular computing (GRC2007), Washington, DC, 2–4 November.
4.
CuiXXuPGuH, et al. (2024) The primary frequency modulation control strategy based on fuzzy active disturbance rejection control for gas turbines. Measurement and Control58(1):15–23.
5.
FaqihMOmarMBIbrahimRB (2022) Development of Rowen’s model for dry-low emission gas turbine dynamic simulation using Scilab. In: 2022 IEEE 5th International symposium in robotics and manufacturing automation (ROMA), Malacca, Malaysia, 6–8 August.
6.
GaoACuiXYuG, et al. (2024) Desired dynamic equation for primary frequency modulation control of gas turbines. Energy Engineering121(5) 1347–1361.
7.
GaoZ (2003) Scaling and bandwidth-parameterization based controller tuning. In: Proceedings of the 2003 American control conference, Denver, CO, 4–6 June.
8.
GuoB-ZZhaoZ-L (2017) Active Disturbance Rejection Control for Nonlinear Systems: An Introduction. John Wiley.
9.
HagrasHA (2004) A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE Transactions on Fuzzy Systems12(4): 524–539.
10.
HanJ (2009) From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics56(3): 900–906.
11.
HuangYXueW (2014) Active disturbance rejection control: Methodology and theoretical analysis. ISA Transactions53(4): 963–976.
12.
JalaliNTolou AskariMRazmiH (2021) Improving the performance of gas turbine based on Rowen model using type-2 fuzzy controller. Journal of Applied Dynamic Systems and Control4(1): 34–42.
KecskésISzékácsLOdryP (2015) Lookup table based fuzzy controller implementation in low-power microcontrollers of hexapod robot Szabad (ka)-II. In: 3rd international conference & workshop mechatronics in practice and education–MECHEDU, Kuantan, Malaysia.
16.
KhormaliAYousefiIYahyaeiH, et al. (2015) Identification of an industrial gas turbine based on Rowen’s model and using multi-objective optimization method. In: 2015 3rd RSI international conference on robotics and mechatronics (ICROM), Tehran, Iran, 7–9 October.
17.
KumariNAryanPRajaGL, et al. (2023) Dual degree branched type-2 fuzzy controller optimized with a hybrid algorithm for frequency regulation in a triple-area power system integrated with renewable sources. Protection and Control of Modern Power Systems8(3): 1–29.
18.
LiL (2023a) A novel optimization control of gas turbine based on a hybrid method using the BELBIC and adaptive multi input multi output feedback control. Journal of Intelligent & Fuzzy Systems45(1): 863–876.
19.
LiX (2023b) Active disturbance rejection control with low power extended state observer and its application to speed control of single shaft gas turbines. Authorea Preprints.
20.
LinPDuXShiY, et al. (2022) Modeling and controller design of a micro gas turbine for power generation. ISA Transactions124: 411–426.
21.
LuoZLiYZhangM, et al. (2024) Improved ADRC based speed control for dynamic performance improvement of micro-turbine system. In: 2024 IEEE 7th international electrical and energy conference (CIEEC), Harbin, China, 10–12 May.
22.
MohammadikiaRAliasgharyM (2019) Design of an interval type-2 fractional order fuzzy controller for a tractor active suspension system. Computers and Electronics in Agriculture167: 105049.
23.
RowenWI (1983) Simplified mathematical representations of heavy-duty gas turbines. Journal of Engineering for Gas Turbines and Power105(4): 865–869.
24.
SaraswatRSuhagS (2023) Type-2 fuzzy logic PID control for efficient power balance in an AC microgrid. Sustainable Energy Technologies and Assessments56: 103048.
25.
ShiGWuZHeT, et al. (2020a) Decentralized active disturbance rejection control design for the gas turbine. Measurement and Control53(9–10): 1589–1601.
26.
ShiGWuZHeT, et al. (2020b) Shaft speed control of the gas turbine based on active disturbance rejection control. IFAC-PapersOnLine53(2): 12523–12529.
27.
SuYChenYLiuY, et al. (2024) Frequency domain delay performance analysis of heavy-duty gas turbine control system: Theory and experiment. IEEE Transactions on Industrial Electronics72(2): 2001–2011.
28.
TanWWChuaTW (2007) Uncertain rule-based fuzzy logic systems: Introduction and new directions (Mendel JM; 2001)[book review]. IEEE Computational Intelligence Magazine2(1): 72–73.
29.
TavakoliMRBVahidiBGawlikW (2009) An educational guide to extract the parameters of heavy duty gas turbines model in dynamic studies based on operational data. IEEE Transactions on Power Systems24(3): 1366–1374.
30.
WangDXiaH (2023) Multivariable control of three-shaft gas turbine based on ADRC. In: 2023 42nd Chinese control conference (CCC), Tianjin, China, 24–26 July.
31.
WangYFangSHuJ, et al. (2023) A novel active disturbance rejection control of PMSM based on deep reinforcement learning for more electric aircraft. IEEE Transactions on Energy Conversion38(2): 1461–1470.
32.
WieseABlomMManzieC, et al. (2015) Model reduction and MIMO model predictive control of gas turbine systems. Control Engineering Practice45: 194–206.
33.
XueWLiuPChenS, et al. (2016) On extended state predictor observer based active disturbance rejection control for uncertain systems with sensor delay. In: 2016 16th international conference on control, automation and systems (ICCAS), Gyeongju, South Korea, 16–19 October.
34.
ZhangBTanWLiJ (2020) Tuning of Smith predictor based generalized ADRC for time-delayed processes via IMC. ISA Transactions99: 159–166.
