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 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.

Missile Guidance Algorithm Design Using Particle Swarm Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 2411-2415
Author(s):  
Chien Chun Kung ◽  
Kuei Yi Chen
Keyword(s):  
Objective Function ◽  
Fitness Function ◽  
Equations Of Motion ◽  
Algorithm Design ◽  
Solution Space ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Missile Guidance ◽  
Pursuit Evasion ◽  
Guidance Algorithm

This paper presents a technique to design a PSO guidance algorithm for the nonlinear and dynamic pursuit-evasion optimization problem. In the PSO guidance algorithm, the particle positions of the swarm are initialized randomly within the guidance command solution space. With the particle positions to be guidance commands, we predict and record missiles’ behavior by solving point-mass equations of motion during a defined short-range period. Taking relative distance to be the objective function, the fitness function is then evaluated according to the objective function. As the PSO algorithm proceeds, these guidance commands will migrate to a local optimum until the global optimum is reached. This paper implements the PSO guidance algorithm in two pursuit-evasion scenarios and the simulation results show that the proposed design technique is able to generate a missile guidance law which has satisfied performance in execution time, terminal miss distance, time of interception and robust pursuit capability.

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