Comparative analysis of PSO algorithms for PID controller tuning

Štimac, Goranka; Braut, Sanjin; Žigulić, Roberto

doi:10.3901/cjme.2014.0527.302

Cited by 28 publications

(9 citation statements)

References 14 publications

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“…Pandey et al [239] presented the load frequency control (LFC) problem using different optimization algorithms for two types of power system configurations. The control scheme proposed was based on linear matrix inequalities with its parameters tuned by PSO.Štimac et al [240] applied PSO to tune PID controller, using active magnetic bearing that suspended the rotating shaft and maintained it in levitated position by applying controlled electromagnetic forces on the rotor in radial and axial directions. Nedic et al [241] proposed a new cascade load force control design for a parallel robot platform.…”

Section: Automatic Controlmentioning

confidence: 99%

“…Ganguly et al [221], Komsiyah [222], Feng et al [223], Pekşen et al [224], Yang et al [225], de Mendonça et al [226], Liu et al [227], Aich and Banerjee [228], Chou et al [229], Lee et al [230], Thakral and Bakhshi [231], Fister et al [232], Aghaei et al [233], Selakov et al [234], Shirvany et al [235], and Tungadio et al [236] Automatic control Cai and Yang [237], Kolomvatsos and Hadjieftymiades [238], Pandey et al [239],Štimac et al [240], Nedic et al [241], Chang and Chen [242], Xiang et al [243], Danapalasingam [244], Mahmoodabadi et al [245], Zhong et al [246], Perng et al [247], Huang and Li [248], and Nisha and Pillai [249] Communication Yousefi et al [250], Sun et al [251], Yongqiang et al [252], Chiu et al [253], Zubair and Moinuddin [255], Kim and Lee [256], Yazgan and Hakki Cavdar [257], Rabady and Ababneh [258], Das et al [259], Scott-Hayward and Garcia-Palacios [260], Omidvar and Mohammadi [261], and Kuila and Jana [262] Operations Liu and Wang…”

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Automatic Controlmentioning

confidence: 99%

Section: Area Publication Electrical and Electronic Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

697

380

View full text Add to dashboard Cite

show abstract

“…In this way, the nature of a cuckoo's egg (modern cuckoo) is distinguished with the progressed way length of the robot. As clarified within the over meta-heuristic improvement issue, each movement to be moved by the robot is decided subordinate on the division between the places of the homes (cuckoo) to the objective and the obstacles show within the cloud condition [28][29][30]. A-When all is said in done, the robot way organizing for the foremost portion depends on the going with two hones, an) Impediment avoiding conduct the hindrance avoidance conduct is utilized to go without from hitting with preventions, (for example, dividers) display within the earth.…”

Section: Cuckoo Search Optimization Algorithmmentioning

confidence: 99%

Optimal Robotic Path Planning Using Intlligents Search Algorithms

AlKhlidi

Abdulsadda

Bakri

2021

jrc

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This investigate investigates the application of Adjusted Fuzyy Molecule Swarm Optimization (FPSO) to the versatile robot route issue in arrange to decide the briefest conceivable course with the least time required to travel from a beginning area to a goal area in a deterrent working zone. MPSO is being created in this ponder to progress the capability of customized calculations for a worldwide course. The proposed calculations decipher the environment outline spoken to by the framework show and develop an ideal or nearly ideal collision-free way. Reenactment tests appear the viability of the most recent organized calculation for portable robot course arranging. The programs are composed in MATLAB R2019a, and run on a 2.65 GHz Intel Center i5 and 7 GB Smash computer. Changes proposed in MPSO and cuckoo look calculation fundamentally point to resolve the untimely merging issue related with the beginning PSO. A mistake calculate is demonstrated within the MPSO to guarantee meeting of the PSO. FPSO points to handle another issue which is the populace may incorporate a few infeasible ways; an updated strategy is tired the FPSO to fathom the issue of the infeasible street. The discoveries illustrate that this calculation has huge potential to fathom the course arranging with satisfactory comes about in terms of decreasing remove and time for execution.

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“…PSO algorithm is very popular for optimizing the controller's parameters. It can optimize the problem with a given fitness function in a shorter period of time and strong controller parameters [13]. In this paper, two PI controllers are proposed to control the rotational speed and electromagnetic torque of the BBLDC motor.…”

Section: Introductionmentioning

confidence: 99%

Simulation Design of Blood-pump Intelligent Controller Based on PID-like fuzzy logic Technique

Raheem¹,

Hassan²,

Kadhim³

2020

ETJ

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This paper presents a blood pump with a bearingless brushless DC motor, supported by speed, torque, and suspension force controllers. Simulation of the pump motor and its controllers tested by MATLAB/Simulink. Two Proportional plus Integral (PI) controllers are employed for controlling the rotational speed and torque of the motor. For controlling the suspension force a comparative study is presented between the Proportional plus Integral plus Derivative (PID) controller and two inputs PID-like Fuzzy Logic Controller (FLC). A particle swarm optimization technique is used to find the best values for the controller’s parameters. The results of the speed and torque controllers exhibit a good time response to reach the desired speed with a short period of time and to decrease the distorting effects of the load torque successfully. Under similar conditions, the PID-like FLC that controls the suspension forces shows a better time response compared to the PID controller. An enhancement in the responses is rated between 18% and 49%, measured using the absolute integral of error criteria on the x and y axes, and in the processing, time rated between 38% and 47%, very high oscillation suppression capability is observed in the PID-like FLC response

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Comparative analysis of PSO algorithms for PID controller tuning

Cited by 28 publications

References 14 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Optimal Robotic Path Planning Using Intlligents Search Algorithms

Simulation Design of Blood-pump Intelligent Controller Based on PID-like fuzzy logic Technique

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