Abstract
This paper presents a novel lightning search algorithm (LSA) using quantum mechanics theories to generate a quantum-inspired LSA (QLSA). The QLSA improves the searching of each step leader to obtain the best position for a projectile. To evaluate the reliability and efficiency of the proposed algorithm, the QLSA is tested using eighteen benchmark functions with various characteristics. The QLSA is applied to improve the design of the fuzzy logic controller (FLC) for controlling the speed response of the induction motor drive. The proposed algorithm avoids the exhaustive conventional trial-and-error procedure for obtaining membership functions (MFs). The generated adaptive input and output MFs are implemented in the fuzzy speed controller design to formulate the objective functions. Mean absolute error (MAE) of the rotor speed is the objective function of optimization controller. An optimal QLSA-based FLC (QLSAF) optimization controller is employed to tune and minimize the MAE, thereby improving the performance of the induction motor with the change in speed and mechanical load. To validate the performance of the developed controller, the results obtained with the QLSAF are compared to the results obtained with LSA, the backtracking search algorithm (BSA), the gravitational search algorithm (GSA), the particle swarm optimization (PSO) and the proportional integral derivative controllers (PID), respectively. Results show that the QLASF outperforms the other control methods in all of the tested cases in terms of damping capability and transient response under different mechanical loads and speeds.
Highlights
Three-phase induction motors (TIMs) are widely used in numerous applications, such as in factories, the industrial sector, air compressors, fans, railway tractions, pumps, and so on, accounting for approximately 60% of the total industrial electricity consumption [1,2]
The results obtained from the developed quantum-inspired LSA (QLSA)-based fuzzy logic controller (FLC) (QLSAF) have been compared to the results obtained by other controllers based on lightning search algorithm (LSA), backtracking search algorithm (BSA), gravitational search algorithm (GSA), particle swarm optimization (PSO) and proportional integral derivative controllers (PID) under sudden changes in speed and mechanical load
A novel QLSA is applied in this study to improve the fuzzy logic speed controller in the induction motor
Summary
Three-phase induction motors (TIMs) are widely used in numerous applications, such as in factories, the industrial sector, air compressors, fans, railway tractions, pumps, and so on, accounting for approximately 60% of the total industrial electricity consumption [1,2]. A novel optimization method called the quantum-behaved lightning search algorithm (QLSA), is generated to solve constrained optimization problems This method applies quantum mechanics theories with LSA to enhance the performance of LSA. The second experiment is developed to improve the performance of the TIM speed controller by tuning the free parameters and selecting the limits and best values for the input and output of the MFs. The results obtained from the developed QLSA-based FLC (QLSAF) have been compared to the results obtained by other controllers based on LSA, BSA, GSA, PSO and PID under sudden changes in speed and mechanical load. Numerous optimization methods are available for developing and improving system performance These methods include GA, simulated annealing, the ant colony search algorithm, PSO, GSA, and so on.
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