A particle swarm optimization approach for optimum design of PID controller in AVR system

By Zwe-Lee Gaing. IEEE transactions on energy conversion, 2004

1,849 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/32d8f043b8155cdb60ee5663c5b5a8f67c730915

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In Control and Systems Engineering

Citations in the corpus

Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design (2007, methodology): “…of each particle can be calculated using the current velocity and the distance from the pbest j,g to gbest g as shown in the following formulas [17]: w = inertia weight factor; c 1 , c 2 = cognitive and social acceleration factors respectively; r 1 , r 2 = random numbers uniformly distributed…”
Multiobjective robust optimization method for drawbead design in sheet metal forming (2010, background): “neutral network, pattern recognition, fuzzy system control and other fields [36–41].”
Tuning and Assessment of Proportional–Integral–Derivative Controller for an Automatic Voltage Regulator System Employing Local Unimodal Sampling Algorithm (2014, methodology): “The range of the PID controller gains for an AVR system mentioned in [6, 11, 15, 17] were 0.”
Analysis of the publications on the applications of particle swarm optimisation (2008, background): “Application areas include automatic generation control tuning, design of controllers, traffic flow control, adaptive inverse control, predictive control, PI and PID controllers [19], strip flatness control, ultrasonic motor control, power plants and systems control [20, 21], control of chaotic syste”
Design of a fractional order PID controller for an AVR using particle swarm optimization (2009, methodology): “Details of the design process are relatively similar to the one reported in Gaing (2004) for a regular PID controller.”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “FA [259] and PSO [260] were used to tune the proportional integral derivative controller for an automatic voltage regulator system.”
Swarm Intelligence in Optimization (2008, background): “al [180] PID controller Gaing [60] Biomedical image registration Wachowiak et.”
A hybrid genetic algorithm and bacterial foraging approach for global optimization (2007, methodology): “The proposed algorithm is validated using four test functions and for PID controller tuning [18,29,17] in Section 3.”
Parameter extraction of solar photovoltaic modules using penalty-based differential evolution (2012, background): “For GA, serious shortcomings, namely low speed and degradation for highly interactive fitness function are reported [35,36].”
Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization (2008, background): “PSO has proved promising in solving a number of engineering problems such as automatic control [20], antenna design [21], and inverse problems [22].”