With the increasing demand for high precision and strong robustness in permanent magnet synchronous motor (PMSM) control systems, this study addresses the performance deficiencies of PMSM in terms of tracking and disturbance rejection. An improved fuzzy proportional–integral–differential (FPID) controller based on an improved moth-flame optimization (IMFO) FPID algorithm (IMFO-FPID) is proposed for PMSM control. In off-line mode, the parameters of the fuzzy logic controller (FLC) are optimized using the IMFO algorithm. The study improves the moth-flame optimization (MFO) algorithm using scrambling Sobol sequences, nonlinear inertia weights, sine cosine algorithm (SCA) and the greedy mechanism. When the motor runs, the FLC dynamically adjusts the proportional–integral–derivative (PID) controller to adapt to changes in the PMSM state. Simulation results demonstrate that the designed IMFO-FPID controller significantly outperforms other controllers under step and sinusoidal response conditions. In addition, the study evaluates the robustness and adaptability of each controller under varying speed and load conditions, where the IMFO-FPID controller exhibits stronger stability and adaptability, further validating its superior performance in practical applications.
AhmedAGMPharrMWonkaP (2023) ART-owen scrambling. ACM Transactions on Graphics42(6): 1–11.
2.
AlzaqebahMAlrefaiNAhmedEAE, et al. (2020) Neighborhood search methods with moth optimization algorithm as a wrapper method for feature selection problems. International Journal of Power Electronics and Drive Systems/international Journal of Electrical and Computer Engineering10(4): 3672.
3.
AmieurTBechouatMSedraouiM, et al. (2021) A new robust tilt-PID controller based upon an automatic selection of adjustable fractional weights for permanent magnet synchronous motor drive control. Electrical Engineering103(3): 1881–1898.
4.
CaoWZhangPMiQ, et al. (2023) Optimization of X-axis servo drive performance using PSO fuzzy control technique for double-axis dicing saw. Scientific Reports13(1): 20719.
5.
ChenPLuoY (2022) Analytical fractional-order PID controller design with BoDe’s ideal cutoff filter for PMSM Speed servo system. IEEE Transactions on Industrial Electronics70(2): 1783–1793.
6.
DingYZhouKBiW (2020) Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer. Soft Computing24(15): 11663–11672.
7.
DuanYYuX (2023) A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems. Expert Systems with Applications213: 119017.
8.
FatemimoghadamAIyerLVKarNC (2024) Real-Time validation of enhanced permanent magnet synchronous motor drive using Dense-Neural-Network-Based control. IEEE Access12: 73323–73339.
9.
GaoHLiJXiongL, et al. (2024) Fuzzy adaptive tuning control of power system based on moth-flame optimization algorithm. Transactions of the Institute of Measurement and Control46(3): 513–523.
10.
GhanayemHAlathamnehMNelmsRM (2023) Three-phase PMSM vector control using decoupled flux and speed controller. Energy Reports9: 645–652.
11.
GopiPReddySVBajajM, et al. (2024) Performance and robustness analysis of V-Tiger PID controller for automatic voltage regulator. Scientific Reports14(1): 7867.
12.
KhanhPQAnhHPH (2023) Advanced PMSM speed control using fuzzy PI method for hybrid power control technique. Ain Shams Engineering Journal14(12): 102222.
13.
KumarSSinghB (2024) Improved MRAC based sensorless PMSM drive with enhanced speed control for LEV application. IEEE Transactions on Energy Conversion.
14.
LiTLiAHouL (2025) Improved GOA-based fuzzy PI speed control of PMSM with predictive current regulation. PLoS ONE20(1): e0318094.
15.
LinGQLiLLTsengML, et al. (2020) An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. Journal of Cleaner Production253: 119966.
16.
LiuXDengYLiH, et al. (2024) Composite control based on FNTSMC and adaptive neural network for PMSM system. ISA Transactions151: 198–211.
17.
LuKZengGZhouW (2021) Adaptive constrained population extremal optimization-based robust proportional-integral-derivation frequency control method for an islanded microgrid. IET Cyber-systems and Robotics3(3): 210–227.
18.
LuKZhouWZengG, et al. (2018) Design of PID controller based on a self-adaptive state-space predictive functional control using extremal optimization method. Journal of the Franklin Institute355(5): 2197–2220.
19.
MaCHuangBBasherMK, et al. (2024) Fuzzy PID Control design of mining electric locomotive based on permanent magnet synchronous motor. Electronics13(10): 1855.
20.
Nadimi-ShahrakiMHZamaniHFatahiA, et al. (2023) MFO-SFR: An enhanced Moth-Flame optimization algorithm using an effective stagnation finding and replacing strategy. Mathematics11(4): 862.
21.
RajFVAKannanVK (2022) Particle swarm optimized deep convolutional neural Sugeno-Takagi Fuzzy PID controller in permanent magnet synchronous motor. International Journal of Fuzzy Systems24(1): 180–201.
22.
SahooSKSahaAKNamaS, et al. (2022) An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy. Artificial Intelligence Review56(4): 2811–2869.
23.
Said-RomdhaneMBSkander-MustaphaSBelhassenR (2023) Adaptive deadbeat predictive control for PMSM-based solar-powered electric vehicles with enhanced stator resistance compensation. Science and Technology for Energy Transition78: 35.
24.
ShehabMAbualigahLAl HamadH, et al. (2020) Moth-flame optimization algorithm: Variants and applications. Neural Computing and Applications32(14): 9859–9884.
25.
SigneRKMottoFB (2024) Fuzzy-PID controller based sliding-mode for suppressing low frequency oscillations of the synchronous generator. Heliyon10(15): e35035.
26.
SirsantSHamoudaMAShaabanMF, et al. (2022) A Chaotic Sobol Sequence-based multi-objective evolutionary algorithm for optimal design and expansion of water networks. Sustainable Cities and Society87: 104215.
27.
SongZYangJMeiX, et al. (2021) Deep reinforcement learning for permanent magnet synchronous motor speed control systems. Neural Computing and Applications33(10): 5409-5418
28.
SunWSiHQiuJ, et al. (2024) Research on efficiency of permanent-magnet synchronous motor based on adaptive algorithm of fuzzy control. Sustainability16(3): 1253.
29.
ThulAGroschupBHameyerK (2020) Influences on the accuracy of torque calculation for permanent magnet synchronous machines. IEEE Transactions on Energy Conversion35(4): 2261–2268.
30.
TranHNJeonJW (2023) Robust speed controller using dual adaptive sliding Mode Control (DA-SMC) method for PMSM drives. IEEE Access11: 63261–63270.
31.
ÜnsalSAliskanI (2022) Investigation of performance of fuzzy logic controllers optimized with the hybrid genetic-gravitational search algorithm for PMSM speed control. Automatika63(2): 313–327.
32.
WangHXuSHuH (2023) PID Controller for PMSM speed control based on improved quantum genetic algorithm optimization. IEEE Access11: 61091–61102.
33.
WangYYuHNiuS, et al. (2024) Adaptive observer-based current constraint control for electric vehicle used PMSM. Applied Energy360: 122802.
34.
XuWJunejoAKTangY, et al. (2021) Composite speed control of PMSM drive system based on finite time sliding mode observer. IEEE Access9: 151803–151813.
35.
ZhangBNiuPGuoX, et al. (2024) Fuzzy PID control of permanent magnet synchronous motor electric steering engine by improved beetle antennae search algorithm. Scientific Reports14(1): 2898.
36.
ZhouJHuangSQiuY (2022) Optimization of random forest through the use of MVO, GWO and MFO in evaluating the stability of underground entry-type excavations. Tunnelling and Underground Space Technology124: 104494.
