Effective PID controller design using a novel hybrid algorithm for high order systems

Abstract

This paper discusses the merging of two optimization algorithms, atom search optimization and particle swarm optimization, to create a hybrid algorithm called hybrid atom search particle swarm optimization (h-ASPSO). Atom search optimization is an algorithm inspired by the movement of atoms in nature, which employs interaction forces and neighbor interaction to guide each atom in the population. On the other hand, particle swarm optimization is a swarm intelligence algorithm that uses a population of particles to search for the optimal solution through a social learning process. The proposed algorithm aims to reach exploration-exploitation balance to improve search efficiency. The efficacy of h-ASPSO has been demonstrated in improving the time-domain performance of two high-order real-world engineering problems: the design of a proportional-integral-derivative controller for an automatic voltage regulator and a doubly fed induction generator-based wind turbine systems. The results show that h-ASPSO outperformed the original atom search optimization in terms of convergence speed and quality of solution and can provide more promising results for different high-order engineering systems without significantly increasing the computational cost. The promise of the proposed method is further demonstrated using other available competitive methods that are utilized for the automatic voltage regulator and a doubly fed induction generator-based wind turbine systems.

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.…”

“…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.…”

A CNN-based model to count the leaves of rosette plants (LC-Net)

“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”

An elite approach to re-design Aquila optimizer for efficient AFR system control

“…The performance index used (F) in this study serves as the cost function for minimization. It is defined as follows [55].…”