Improvement of Buck Converter Performance Using Artificial Bee Colony Optimized-PID Controller

2015 ◽  
pp. 304-310 ◽  
Author(s):  
Yusuf Sonmez ◽  
Ozcan Ayyildiz ◽  
H. Tolga Kahraman ◽  
Ugur Guvenc ◽  
Serhat Duman
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony ◽  
Buck Converter ◽  
Bee Colony

scholarly journals Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3385
Author(s):  
Erickson Puchta ◽  
Priscilla Bassetto ◽  
Lucas Biuk ◽  
Marco Itaborahy Filho ◽  
Attilio Converti ◽  
...  
Keyword(s):  
Pid Controller ◽  
Optimization Algorithms ◽  
Optimization Methods ◽  
Buck Converter ◽  
Algebraic Solution ◽  
Nonlinear Load ◽  
Bee Colony ◽  
Arduous Task ◽  
Whale Optimization

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.

Robust PID controller design based on H∞ theory and a novel constrained artificial bee colony algorithm

2016 ◽  
Vol 40 (1) ◽  
pp. 202-209 ◽  
Author(s):  
Vahid Bijani ◽  
Alireza Khosravi
Keyword(s):  
Pid Controller ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Robust Performance ◽  
Bee Colony ◽  
New Strategy ◽  
Pid Controller Design ◽  
High Flexibility ◽  
Setting Parameters

This paper presents a new strategy for tuning the coefficients of a PID controller regarding H∞ robust performance and stability constraints based on the constrained artificial bee colony (CABC) algorithm. First, the issue of tuning the PID controller to follow H∞ specifications are introduced, and the objective function and constraints of the optimization problem are specified. Then, a simple and efficient method of transforming the constrained optimization problem to an unconstrained one is presented, and used within the CABC for optimization. The CABC is one of the most recently introduced optimization algorithms, and has the advantages of strong robustness, fast convergence and high flexibility, with fewer setting parameters. The algorithm is essentially a random and intelligent evolutionary method. This method is also utilized and simulated in several models. The simulation results have been compared with other techniques, which demonstrate the efficiency and superiority of the proposed scheme.

Optimal Tuning of Robust Controller Based on Artificial Bee Colony Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1668-1672 ◽  
Author(s):  
Kun Xu ◽  
Ming Yan Jiang
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Proportional Integral Derivative ◽  
Robust Controller ◽  
Optimal Tuning ◽  
Bee Colony ◽  
Constraint Factors ◽  
Controller Performance ◽  
Swarm Intelligence Algorithm

Artificial bee colony algorithm (ABCA) is a novel swarm intelligence algorithm for global optimization. An efficient method based on ABCA is proposed for optimal tuning of robust proportional-integral-derivative (PID) controller in this paper. A multi-objective optimization is applied to balance several constraint factors of controller. Performance of robust PID controller is evaluated by a three-order and time-delay transfer function with 40%, 50% and 60% uncertainty, respectively. Simulation result clearly demonstrates that the designed controller can obtain an optimal tuning and endure parameter fluctuation. From comparisons of robust PID controller in different uncertainties, a conclusion can be obtained that performance of robust PID controller will be worse as the uncertainty increases, even obtain a divergent solution when the uncertainty is more than 60%.

scholarly journals Optimal PID controller design using artificial bee colony algorithm for robot arm

2021 ◽  
Vol 21 (1) ◽  
pp. 84
Author(s):  
Ghassan A. Sultan ◽  
Muhammed K. Jarjes
Keyword(s):  
Pid Controller ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Controller Design ◽  
Dynamic Performance ◽  
Control Process ◽  
Robot Arm ◽  
Bee Colony ◽  
Pid Controller Design ◽  
Best Parameter

<p class="Default"><span>Proportional integral derivation (PID) controller is used in this paper for optimal design, and tuning by zeigler and nichol (ZN) with artificial bee colony algorithm. The best parameter were found using these algorithms for best performance of a robot arm. The advantage of using ABC were highlighted. The controller using the new algorithm was tested for valid control process. Different colony size has been performed for tuning process, settling time, from time domain performance, rise time, overshot, and steady state error with ABC tuning give better dynamic performance than controller using the (ZN).</span></p>

Sign in / Sign up

Export Citation Format

Share Document