Optimization of Fractional Order Controllers for AVR System Using Distance and Levy-Flight Based Crow Search Algorithm

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

“…In addition to the comparative assessment against the algorithms used in the above analyses, further comparisons were made with recently reported optimization techniques in the literature (See Table 11 ). This techniques include hybrid atom search particle swarm optimization (h-ASPSO) based PID controller [ 46 ], improved marine predators algorithm (MP-SEDA)-tuned FOPID controller [ 47 ], modified artificial bee colony (IABC) based LOA-FOPID [ 48 ], equilibrium optimizer (EO) based TI λ DND 2 N 2 [ 23 ], whale optimization algorithm (WOA) based PIDA [ 49 ], symbiotic organism search (SOS) algorithm-based PID-F controller [ 50 ], mayfly optimization algorithm based PI λ1 I λ2 D μ1 D μ2 controller [ 25 ], Levy flight improved Runge-Kutta optimizer (L-RUN) based PIDD 2 controller with master/slave approach [ 51 ], particle swarm optimization based 2DOF-PI controller with amplifier feedback [ 52 ], modified artificial rabbits optimizer (m-ARO) based FOPIDD 2 controller [ 53 ], genetic algorithm (GA) based fuzzy PID controller [ 54 ], sine-cosine algorithm (SCA) based FOPID controller with fractional filter [ 55 ], imperialist competitive algorithm (ICA) based gray PID controller [ 56 ], Rao algorithm based multi‐term FOPID controller [ 57 ], whale optimization algorithm (WOA) based 2DOF-FOPI [ 58 ], chaotic yellow saddle goatfish algorithm (C-YSGA) based FOPID controller [ 59 ] and crow search algorithm (CSA) based FOPI controller [ 31 ]. The results indicate that the QWGBO algorithm outperforms several state-of-the-art optimization methods, demonstrating its effectiveness in AVR system control.…”

“…The proposed approach excels in transient response and frequency response, outperforming state-of-the-art control methods. In [ 31 ], the distance and Levy-flight based crow search algorithm (DLCSA) optimizes FOPID, FOPI, and FOPD controllers for AVR systems, showcasing superior performance in various aspects compared to established techniques.…”

“…To validate the proposed approach’s superiority, extensive comparative analyses (statistical, boxplot, convergence profile, Wilcoxon signed-rank test, transient and frequency responses, performance against varying input reference and external load disturbance, controller effort and robustness) were conducted against other competitive algorithms and recently reported optimization techniques. In addition to the comparative assessment against the algorithms used in the above analyses, further comparisons were made with recently reported 17 optimization techniques in the literature [ 23 , 25 , 31 , 46 – 59 ]. The simulation results unequivocally highlight the QWGBO algorithm’s superior performance in optimizing the AVR system, as evident from lower objective function values, excellent convergence, and statistical assessments as well as stability and robustness analyses.…”

Temperature Compensation of Laser Methane Sensor Based on a Large-Scale Dataset and the ISSA-BP Neural Network

“…The anti-predator individual in this formulation is at the current global optimum, so the search range of its particles is reduced, increasing the probability that the algorithm is premature. To solve the aforementioned problems, this paper introduces the Levy flight strategy [ 32 ] with a randomized step size, which can achieve a more extensive search area when searching in an unknown location, thus improving the global search capability of the anti-predator. The improved anti-predator position update formula is where is the location of the current optimal solution; denotes the randomized step size after repeated experiments to take the value of 0.55; f g denotes the current global best fitness value.…”

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

“…As is the case for the DC motor, a controller is also required for efficient operation of an AVR system. In that respect, several controller structures such as PID controller (Bingul and Karahan, 2018; Zhou et al, 2019), FOPID controller (Bhookya and Jatoth, 2019; Bhullar et al, 2020b), PID plus second order derivative (PIDD 2 ) controller (Mokeddem and Mirjalili, 2020; Mosaad et al, 2018) and derivatives of PID controller (Moschos and Parisses, in press; Paliwal et al, 2021) along with neural network predictive control (Elsisi, 2019) and state feedback controller (Eke et al, 2021; Gozde, 2020; Mary et al, 2021) have so far been utilized. Similar to the case in DC motors, the PID controller is the most preferred controller for an AVR system, as well (Ayas, 2019).…”