Journal ArticleDOI
A particle swarm optimization approach for optimum design of PID controller in AVR system
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TLDR
The proposed PSO method was indeed more efficient and robust in improving the step response of an AVR system and had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency.Abstract:
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.read more
Citations
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Journal ArticleDOI
Comparative performance analysis of artificial bee colony algorithm for automatic voltage regulator (AVR) system
TL;DR: It is shown that the ABC algorithm is successfully applied to the AVR system for improving the performance of the controller and shows a better tuning capability than the other similar population based optimization algorithms for this control application.
Journal ArticleDOI
Optimal Switch Placement in Distribution Systems Using Trinary Particle Swarm Optimization Algorithm
TL;DR: A novel three-state approach inspired from the discrete version of a powerful heuristic algorithm, particle swarm optimization, is developed and presented to determine the optimum number and locations of two types of switches in radial distribution systems.
Journal ArticleDOI
Design of a Self-Tuning PI Controller for a STATCOM Using Particle Swarm Optimization
Chien-Hung Liu,Yuan-Yih Hsu +1 more
TL;DR: A self-tuning proportional-integral (PI) controller in which the controller gains are adapted using the particle swarm optimization (PSO) technique is proposed for a static synchronous compensator (STATCOM).
Journal ArticleDOI
Tuning of PID Controller Using Particle Swarm Optimization (PSO)
TL;DR: The results show the advantage of the PID tuning using PSO-based optimization approach, compared to the conventional gain tuning using Ziegler-Nichols method.
Journal ArticleDOI
Performance analysis of fractional order fuzzy PID controllers applied to a robotic manipulator
TL;DR: Numerical simulation results clearly indicate the superiority of FOFPID controller over the other controllers for trajectory tracking, model uncertainties, disturbance rejection and noise suppression.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Proceedings ArticleDOI
Empirical study of particle swarm optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: The experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.
Book
Power System Analysis
TL;DR: This is the first text in this area to fully integrate MATLAB and SIMULINK throughout and provides students with an author-developed POWER TOOLBOX DISK organized to perform analyses and explore power system design issues with ease.
Book
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
TL;DR: In-depth and updated, Evolutionary Computation shows you how to use simulated evolution to achieve machine intelligence and carefully reviews the "no free lunch theorem" and discusses new theoretical findings that challenge some of the mathematical foundations of simulated evolution.