Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Deviaene, Jasper; Ceulemans, David; Derammelaere, Stijn; Stockman, Kurt

doi:10.1109/tmech.2021.3104327

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Vanchinathan and Selvaganesan 9 proposed a Fractional Order PID controller optimized by artificial ant colony algorithm in the self-adjusting regulator structure to solve the poor controllability of Brushless DC motor caused by long stability time, fluctuation of steady-state error, power fluctuation and other factors in practical application. In Deviaene et al, 10 a PID algorithm is proposed to control and estimate the load angle technology. This technology can realize sinusoidal current supply without position feedback or user input, so as to improve the energy efficiency of Brushless DC motor.…”

Section: Introductionmentioning

confidence: 99%

“…In Refs., [9][10][11][12][13][14][15] the related research of PID control in motor load system is mainly introduced. Vanchinathan and Selvaganesan 9 proposed a Fractional Order PID controller optimized by artificial ant colony algorithm in the self-adjusting regulator structure to solve the poor controllability of Brushless DC motor caused by long stability time, fluctuation of steady-state error, power fluctuation and other factors in practical application.…”

Section: Introductionmentioning

confidence: 99%

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Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

Isen,

Duman

2024

Soft Comput

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This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

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Data-Driven Optimal Tuning of BLDC Motors With Safety Constraints: A Set Membership Approach

Busetto

Lucchini

Formentin

et al. 2023

IEEE/ASME Trans. Mechatron.

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Sensorless Load Angle Control for Energy Optimal Sinusoidal Driven BLDC Motor Applications

Cited by 4 publications

References 30 publications

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Optimization of the different controller parameters via OBL approaches based artificial ecosystem optimization involving fitness distance balance guiding mechanism for efficient motor speed regulation of DC motor

Data-Driven Optimal Tuning of BLDC Motors With Safety Constraints: A Set Membership Approach

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