Sensored speed control of brushless DC motor based salp swarm algorithm

Abstract

<p>This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.</p>

An intelligent PID controller tuning for speed control of BLDC motor using driving training-based optimization

“…Thus, this stage demonstrates DTBO's ability to take use of local search. Using (27), a position is first generated over each population member in order to represent this mathematically.…”

“…However, literature review reveals that various optimization algorithms do exist to optimize any controller for solving any real-world application. A wide range of algorithms, including the genetic algorithm (GA) [13], [14] the particle swarm optimization (PSO) [15], [16], the ant colony Int J Pow Elec & Dri Syst ISSN: 2088-8694  An intelligent PID controller tuning for speed control of BLDC motor … (Hrishikesh Sarma) 2475 optimization (ACO) [17], the modified differential evolution [18], the teaching-learning-based optimization (TLBO) [19], the firefly algorithm (FA) [20], the bacterial foraging (BF) [21], the artificial bee colony optimization (ABC) [22], the simulated annealing (SA) [23], the grey wolf optimization (GWO) [24], the whale optimization algorithm (WOA) [25], the flower pollination [26], the salp swarm algorithm (SSA) [27], and the coronavirus optimization algorithm (COA) [28] have been implemented for controller tuning in achieving speed control of a BLDC motor. All of these studies have come to the conclusion that choosing an appropriate optimization algorithm is crucial for improving the control ability of any controller type for a BLDC motor.…”