Adaptive fractional order PID controller tuning for brushless DC motor using Artificial Bee Colony algorithm

Vanchinathan, K.; Selvaganesan, N.

doi:10.1016/j.rico.2021.100032

Cited by 97 publications

(39 citation statements)

References 27 publications

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“…Instead of soft changing reference signal figure (12) shows the results under sharp step reference where the response confirms the same result of the soft changing reference signal. Table (3) confirms the results of table (2) where the FOPID is better than PID control.…”

Section: Bldc Motorsupporting

confidence: 64%

“…Xiaohong Nian, Fei Peng et al [12] presented an article to Regenerative braking can enhance energy efficiency and can extend the driving range of the EVs. BLDC motors control utilizes the proportional-integral-derivative (PID) control, and the overall braking force considers fuzzy logic controller.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Figure(9,10,11,12,13,14) present Voltage of PID compared to FOPID on step response Generally, the previous relation does not offer the best result in terms of the optimization of the PID controller. By adding a variable , this optimization (maximization of integral gain ki) is improved in conditions of reaching better performance.…”

mentioning

confidence: 94%

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AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

Aboshosha

Hamed

2022

Preprint

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Within the next decades the Electric Vehicle (EV) will dominate the cars market, may be before 2040, where all countries will cope with the zero emissions policy of international institutions to reduce the destructive influence of the climate change. The EVs are simpler in construction and control compared with the Internal Combustion Engine (ICE) vehicles. EV’s speed is the most important factors to be controlled and optimized. Throughout this research article the Fractional Order PID (FOPID) controller has been used to regulate the speed of the EV while meta-heuristic techniques (AEFA, AEO, SMA, GOA, AND WCA) are used to optimize the performance of the EV using Brush Less Direct Motor (BLDC).

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Section: Bldc Motorsupporting

confidence: 64%

Section: Literature Reviewmentioning

confidence: 99%

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AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

Aboshosha

Hamed

2022

Preprint

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“…1 . The mathematical model of an ideal motor requires the assumption that the motor body satisfies the following conditions 32 : (1) ignore the saturation of the motor iron core, (2) ignore the eddy current and hysteresis losses in the motor; (3) the current in the motor is the three-phase symmetric sinusoidal current; and (4) the effects of temperature, frequency variation, and winding damping on resistance are not considered. The three-phase winding voltage equation may be expressed as: where, , , and R denotes the phase voltage, phase current, back electromotive force, and phase impedance of the stator windings, respectively; L and M represent the self-inductance and pairwise mutual inductance of the three-phase windings, respectively.…”

Section: Mathematical Mode Of Bldcmmentioning

confidence: 99%

A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

Wang

et al. 2022

Sci Rep

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In order to enhance the speed control performance of the brushless DC motor (BLDCM), a novel proportion integration differentiation (PID) is proposed in this paper by using dual fuzzy logic systems (FLSs) with harmony search algorithm (HSA) optimization, which is called DFPID-HSA. Firstly, the FLS1 in DFPID-HSA locks the three coefficients of the PID controller in an extensive range on the basis of the system error and error change rate. Then, the FLS2 is optimized by HSA (HSA-F2) to obtain the precise correction of the three coefficients. To get the optimal global harmony better, the improved dynamic adjustment mode is used for the pitch adjustment rate (PAR) and distance bandwidth (BW) in HSA, and the triple selection method is adopted in the composition harmony section to realize the global search. Finally, DFPID-HSA provides the optimal supply control signal to BLDCM so that it can control the speed effectively. Moreover, the stability of the system is analyzed by the pole, Lyapunov, and Nyquist determination methods. And the sensitivity analysis of DFPID-HSA is carried out under the condition of different motor’s mechanical parameters to check its robustness. In addition, the superiority of DFPID-HSA is verified by MATLAB simulation and experiment platform.

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“…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. In Deviaene et al, 10 a PID algorithm is proposed to control and estimate the load angle technology.…”

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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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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Adaptive fractional order PID controller tuning for brushless DC motor using Artificial Bee Colony algorithm

Cited by 97 publications

References 27 publications

AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

AI based Optimization of EV Control based on Fractional Order PID Controller and Meta-Heuristic Techniques

A novel PID controller for BLDCM speed control using dual fuzzy logic systems with HSA optimization

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

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