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
Fractional-Neuro-Optimizer: A Neural-Network-Based Optimization Method
TL;DR: A new procedure for stochastic optimization by a recurrent ANN is introduced, called fractional-neuro-optimizer (FNO), which starts with an initial solution and adjusts the network’s weights by a new heuristic and unsupervised rule to reach a good solution.
Journal ArticleDOI
Rapid generation of control parameters of Multi-Infeed system through online simulation
TL;DR: Simulated Self-Generated - Particle Swarm optimization (SSG-PSO) toolbox that automatically generates PI control parameters very quickly in PSCAD is designed and the results are showing markedly improved performance of a system during startup and under fault condition.
Book ChapterDOI
Tracking multiple targets with adaptive swarm optimization
Jun Liu,Hongbin Ma,Xuemei Ren +2 more
TL;DR: Inspired by particle swarm optimization, the proposed algorithm of tracking multiple targets adaptively modifies the covered radii of each subgroup in terms of the minimum distances among the subgroups, and successfully tracks the conflicting targets.
Proceedings ArticleDOI
Optimal design of current and voltage controllers for a distributed energy resource
TL;DR: In this paper, the optimal design of controllers of the general type of PID for achieving optimal tracking of a constant or slow-changing setpoint is presented for the first and second-order plants.
Journal ArticleDOI
The design of PID controllers for a Gryphon robot using four evolutionary algorithms: a comparative study
TL;DR: This paper compares performances of PID controllers designed for a Gryphon robot joints using four hybrid evolutionary algorithms, and finds that the queen-bee for joints 1, 2, and 4, the genetic algorithm for joint 3, and the shuffled complex evolution method for joint 5 produce better results.
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.