Quantum-Behaved Lightning Search Algorithm to Improve Indirect Field-Oriented Fuzzy-PI Control for IM Drive

• Published in: IEEE transactions on industry applications Vol. 54; no. 4; pp. 3793 - 3805
• Main Authors: Hannan, Mahammad A., Ali, Jamal A., Mohamed, Azah, Amirulddin, Ungku Anisa Ungku, Tan, Nadia Mei Lin, Uddin, Mohammad Nasir
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Algorithms; Computer simulation; Damping; Fitness; Fuzzy control; Fuzzy logic; Fuzzy logic speed controller; indirect field-oriented control (IFOC); induction motor (IM); Induction motors; Optimization; Particle swarm optimization; Performance enhancement; Proportional integral; proportional-integral (PI) controller; quantum-behaved lightening search algorithm (QLSA); Rotor speed; Rotors; Search algorithms; Stators; Torque; Voltage control
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2018.2821644

The main objective of this study is to develop a quantum-behaved lightening search algorithm (QLSA) to improve the indirect field-oriented fuzzy-proportional-integral (PI) controller technique to control a three-phase induction motor (TIM) drive. The generated adaptive PI current control parameters and fuzzy membership functions are carried to design induction motor drive speed controller to minimize the fitness function formulated by QLSA. An optimal QLSA-based indirect field-oriented control (QLSA-IFOC) fitness function is used to reduce the mean absolute error of the rotor speed to improve the performance of the TIM with varying speed and mechanical load. Results obtained from the QLSA-IFOC are compared with those obtained through lightening search algorithm, gravitational search algorithm, backtracking search algorithm, and particle swarm optimization to validate the developed controller. The optimization results of objective functions in terms of box plots and iterations show that the QLSA algorithm outperforms the other optimization algorithms. Moreover, the QLSA-IFOC controller performed well in all tests in terms of transient response. The developed controller also minimizes overshoot, increases damping capability, and reduces the root-mean-square error, as well as standard deviation under sudden change of speed and mechanical loads. A comparative analysis is performed between simulation and experimental results to justify the efficiency of the developed controller.