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
Opposition-based discrete action reinforcement learning automata algorithm case study: optimal design of a PID controller
TL;DR: The opposition-based DARLA method is proposed to design a proportional-integral-derivative (PID) controller for the automatic voltage regulator system and the experimental results demonstrate the superior performance of the proposed approach.
Proceedings ArticleDOI
Design of MIMO controller for a manipulator using Tabu Search algorithm
TL;DR: Simulation results demonstrate that the proposed TS method compared with other heuristic method, i.e., the genetic algorithm (GA) is more efficient in terms of improving the step response of the robot.
Journal ArticleDOI
Implementation of PID controller for liquid level system using mGWO and integration of IoT application
TL;DR: In this article , a real-time liquid level monitoring and control in a single tank system using a modified Grey Wolf Optimization (mGWO) algorithm is used to manage the liquid level.
Proceedings ArticleDOI
PSO-Based Optimization of State Feedback Tracking Controller for a Flexible Link Manipulator
TL;DR: An intelligent method to resolve the problem of choosing elements of Q and R matrices in the state feedback control design using LQR method by adopting PSO-based optimization is proposed.
Proceedings ArticleDOI
The Application of PID Control in Motion Control of the Spherical Amphibious Robot
TL;DR: In this paper, a neutral network PID controller was proposed to track a spherical amphibious robot by combining the neutral network method and traditional PID control to reach the goal of tracking online fast.
References
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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.
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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.