An examination of the impact of the selected error criterion on the ACO performance in BLDC drives
DOI:
https://doi.org/10.58916/jhas.v8i5.56Keywords:
Brushless DC motor (BLDC), PID controllers, Speed Control, Ant Colony (ACO), PID, Inner Loads, Moment of InertiaAbstract
Brushless direct current (BLDC) motor drives are becoming increasingly popular in motion control applications. As a result, a low-cost but effective BLDC motor speed controller is required. They are used in industrial and home appliances with conventional motor drive technology, such as robots, refrigerators and air conditioning systems. Brushless direct current (BLDC) drives are well known for their high efficiency and low maintenance. This work aims to design a non-conventional PID controller via Ant Colony Optimisers (ACO) in order to obtain an efficient speed performance for a BLDC motor "namely, Maxon EC flat". Moreover, when ACO is used for tuning of PID control, the objective function is required. Therefore, in this work, four different objective functions which are MSE, ISE, IAE, and ITAE are applied and their performance was compared in terms of motor' speed stability. In a simulation environment, the PID controller is used to control the speed of BLDC motors under three testing conditions which are Small, medium, and high inertia loads. The simulation results demonstrate that MSE offers the best performance under low and high inertial loading cases. Whereas, IAE has been superior compared with other criterions when medium loads are applied. Moreover, this paper shows a stability speed- power consumption trade-off problem. In other words, achieving a fast stability via any type of error indices doesn’t guarantee a low amount of consuming energy.
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References
V. Manzolini, et al., "Improving the torque generation in self-sensing BLDC drives by shaping the current waveform," in 2016 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM), 2016, pp. 510-515.
R. ÇELİKEL, et al., "Implementation of a flywheel energy storage system for space applications," Turkish Journal of Electrical Engineering and Computer Sciences, vol. 25, pp. 1197-1210, 2017.
U. Ansari and S. Alam, "Modeling and control of three phase BLDC motor using PID with genetic algorithm," in 2011 UkSim 13th international conference on computer modelling and simulation, 2011, pp. 189-194.
A. S. O. Al-Mashakbeh, "Proportional integral and derivative control of brushless DC motor," European Journal of Scientific Research, vol. 35, pp. 198-203, 2009.
K. H. Ang, et al., "PID control system analysis, design, and technology," IEEE Transactions on Control Systems Technology, vol. 13, pp. 559-576, 2005.
S. Sheel and O. Gupta, "New techniques of PID controller tuning of a DC motor—development of a toolbox," MIT International Journal of Electrical and Instrumentation Engineering, vol. 2, pp. 65-69, 2012.
B. Chalmers, "Influence of saturation in brushless permanent-magnet motor drives," in IEE Proceedings B-Electric Power Applications, 1992, pp. 51-52.
C. T. Johnson and R. D. Lorenz, "Experimental identification of friction and its compensation in precise, position controlled mechanisms," IEEE transactions on industry applications, vol. 28, pp. 1392-1398, 1992.
H. Ibrahim, et al., "Optimal PID control of a brushless DC motor using PSO and BF techniques," Ain Shams Engineering Journal, vol. 5, pp. 391-398, 2014.
R. V. Jain, et al., "Tuning of fractional order PID controller using particle swarm optimization technique for DC motor speed control," in 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2016, pp. 1-4.
M. Mavrovouniotis, et al., "Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [research frontier]," IEEE Computational Intelligence Magazine, vol. 15, pp. 52-63, 2020.
J. Sang, "A Cost-effective Pump Scheduling Method for Mine Drainage System Based on Ant Colony Optimization," Journal Européen des Systèmes Automatisés, vol. 52, pp. 123-128, 2019.
A. H. Musbah and H. Alzarok, "Tuning of a Speed Control System for DC Servo Motor Using Genetic Algorithm."
T. Samakwong and W. Assawinchaichote, "PID controller design for electro-hydraulic servo valve system with genetic algorithm," Procedia Computer Science, vol. 86, pp. 91-94, 2016.
U. K. Bansal and R. Narvey, "Speed control of DC motor using fuzzy PID controller," Advance in Electronic and Electric Engineering, vol. 3, pp. 1209-1220, 2013.
A. K. Rajagiri, et al., "Speed control of dc motor using fuzzy logic controller by pci 6221 with matlab," in E3S Web of Conferences, 2019, p. 01004.
H. Alzarok, "Towards achieving an optimum speed performance for DC servo motors via Fuzzy logic controllers," in 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), 2022, pp. 1-8.
S. A. Gunawan, et al., "Optimal fractional-order PID for DC motor: Comparison study," in 2018 4th International Conference on Science and Technology (ICST), 2018, pp. 1-6.
R. Singh, et al., "Fractional order PID control using ant colony optimization," in 2016 IEEE 1st International conference on power electronics, intelligent control and energy systems (ICPEICES), 2016, pp. 1-6.
I. Kurniawan, et al., "Tuning fractional order proportional integral derivative controller for DC motor control model using cross-entropy method," in 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018, pp. 351-356.
A. Mirzal, et al., "PID parameters optimization by using genetic algorithm," arXiv preprint arXiv:1204.0885, 2012.
A. Kumar and R. Gupta, "Tuning of PID controller using PSO algorithm and compare results of integral errors for AVR system," International Journal of Innovative Research and Development (ISSN 2278–0211), vol. 2, pp. 58-68, 2013.
J. Yang and Y. Zhuang, "An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem," Applied Soft Computing, vol. 10, pp. 653-660, 2010.
A. Gupta, "A Novel Evolutionary Neural Network Based Temperature Forecaster Using Ant Colony Optimization Metaheuristic," International Review on Computers and Software, vol. 6, pp. 481-485, 2011.
M. Dorigo, et al., "Ant algorithms for discrete optimization," Artificial life, vol. 5, pp. 137-172, 1999.
G. Chandrasekaran, et al., "Test scheduling of core based system-on-chip using modified ant colony optimization," Journal Européen des Systèmes Automatisés, vol. 52, pp. 599-605, 2019.
N. Hamouda, et al., "Optimal tuning of fractional order proportional-integral-derivative controller for wire feeder system using ant colony optimization," Journal Européen des Systèmes Automatisés, vol. 53, pp. 157-166, 2020.
Y. Lee, et al., "PID controller tuning for integrating and unstable processes with time delay," Chemical engineering science, vol. 55, pp. 3481-3493, 2000.