Optimal tuning of the PID controller for a buck converter using Bacterial Foraging Algorithm

2010 ◽  
Author(s):  
Abolfazl Jalilvand ◽  
Hesan Vahedi ◽  
Akbar Bayat
Keyword(s):  
Pid Controller ◽  
Buck Converter ◽  
Optimal Tuning ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

scholarly journals Optimally Tuned PID Controller of A DC Motor System using Bacterial Foraging Algorithm

2018 ◽  
Vol 8 (02) ◽  
Author(s):  
Rashid Alzuabi
Keyword(s):  
Pid Controller ◽  
Motor System ◽  
Dc Motor ◽  
System Response ◽  
Proportional Integral Derivative ◽  
Close Loop System ◽  
System Description ◽  
Proportional Integral Derivative Controller ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

This paper presents an exercise in applying the bacterial foraging algorithm (BFA) optimisation method on a proportional—integral-derivative controller (PID) of a DC motor circuit. The paper presents the system description of the DC motor transfer function and the simulation of the close loop system using MATLAB. The BFA algorithm is described and discussed with the simulation results presented to illustrate the enhancement of the system response that in result enhances the operation of the DC motor system.

A COMPARISON BETWEEN QUANTUM INSPIRED BACTERIAL FORAGING ALGORITHM AND GA-LIKE ALGORITHM FOR GLOBAL OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250016 ◽  
Author(s):  
SHARINA HUANG ◽  
GUOLIANG ZHAO
Keyword(s):  
Pid Controller ◽  
Population Based ◽  
Voltage Regulator ◽  
Search Technique ◽  
Bacterial Foraging ◽  
Avr System ◽  
Higher Dimensional ◽  
Engineering Problems ◽  
Bacterial Foraging Algorithm ◽  
Stochastic Search Technique

Bacterial foraging algorithm (BFA) is a population-based stochastic search technique for solving various scientific and engineering problems. However, it is inefficient in some practical situations. In order to improve the performance of the BFA, we propose a novel optimization algorithm, named quantum inspired bacterial foraging algorithm (QBFA), which applies several quantum computing principles, and a new mechanism is proposed to encode and observe the population. The algorithm has been evaluated on the standard high-dimensional benchmark functions in comparison with GA, PSO, GSO and FBSA, respectively. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR) system. Simulation results clearly illustrate that the proposed approach is very efficient and could be easily extended to 300 or higher-dimensional problems.

scholarly journals Optimized PID Controller with Bacterial Foraging Algorithm

2015 ◽  
Vol 5 (6) ◽  
pp. 1372
Author(s):  
Seiyed Mohammad Mirzaei ◽  
Mohammad Hossein Moattar
Keyword(s):  
Pid Controller ◽  
Fuzzy Controller ◽  
Equations Of Motion ◽  
Pso Algorithm ◽  
Control Fish ◽  
Motion Sensors ◽  
Fish Robot ◽  
Bacterial Foraging ◽  
Research Findings ◽  
Bacterial Foraging Algorithm

<p><em>Fish robot precision depends on a variety of factors including the precision of motion sensors, mobility of links, elasticity of fish robot actuators system, and the precision of controllers. Among these factors, precision and efficiency of controllers play a key role in fish robot precision.  In the present paper, a robot fish has been designed with dynamics and swimming mechanism of a real fish. According to equations of motion, this fish robot is designed with 3 hinged links. Subsequently, its control system was defined based on the same equations. In this paper, an approach is suggested to control fish robot trajectory using optimized PID controller through Bacterial Foraging algorithm, so as to adjust the gains. Then, this controller is compared to the powerful Fuzzy controller and optimized PID controller through PSO algorithm when applying step and sine inputs. The research findings revealed that optimized PID controller through Bacterial Foraging Algorithm had better performance than other approaches in terms of decreasing of the settling time, reduction of the maximum overshoot and desired steady state error in response to step input. Efficiency of the suggested method has been analyzed by MATLAB software.</em></p>

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.

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

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