Landsman Converter Based Particle Swarm Optimization Technique

This paper proposes a novel control technique for landsman converter using particle swarm optimization. The controller parameters are optimized by pso algorithm,the proposed algorithm is compared with pid controller and the comparative results are presented. Simulation results shows the dynamic performance of pso controller. landsman converter reduction in output voltage ripple in the order of mV along with reduced settling time as compared to the conventional pid controller . The simulated results are executed in MATLAB/SIMULINK.

Download Full-text

Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

International Journal for Research in Engineering Application & Management ◽

10.35291/2454-9150.2020.0279 ◽

2020 ◽

pp. 184-188

Keyword(s):

Particle Swarm Optimization ◽

Pid Controller ◽

Optimization Technique ◽

Heuristic Method ◽

Particle Swarm ◽

Absolute Error ◽

Pso Algorithm ◽

Computational Method ◽

Swarm Optimization ◽

Tuning Method

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

Download Full-text

A Novel Multi-Epoch Particle Swarm Optimization Technique

Cybernetics and Information Technologies ◽

10.2478/cait-2018-0039 ◽

2018 ◽

Vol 18 (3) ◽

pp. 62-74 ◽

Cited By ~ 8

Author(s):

Romasevych Yuriy ◽

Loveikin Viatcheslav

Keyword(s):

Particle Swarm Optimization ◽

Optimization Technique ◽

Particle Swarm ◽

Pso Algorithm ◽

Global Optimum ◽

High Rate ◽

Local Optimum ◽

Swarm Optimization ◽

Particle Swarm Optimization Technique ◽

Great Possibility

Abstract Since canonical PSO method has many disadvantages which do not allow to effectively reach the global minima of various functions it needs to be improved. The article refers to a novel Multi-Epoch Particle Swarm Optimization (ME-PSO) technique which has been developed by authors. ME-PSO algorithm is based on reinitializing of the stagnant swarm with low exploration efficiency. This approach provides a high rate of global best changing. As a result ME-PSO has great possibility of finding good local (or even global) optimum and does not trap in bad local optimum. In order to prove the advantages of the ME-PSO technique numerical experiments have been carried out with ten uni- and multimodal benchmark functions. Analysis of the obtained results convincingly showed significant superiority of ME-PSO over PSO and IA-PSO algorithms. It has been set that canonical PSO is a special case of ME-PSO.

Download Full-text

Performance of PID Controller Tuning based on Particle Swarm Optimization and Firefly Algorithm

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c1042.1083s219 ◽

2019 ◽

Vol 8 (3S2) ◽

pp. 225-230 ◽

Cited By ~ 1

Keyword(s):

Particle Swarm Optimization ◽

Pid Controller ◽

Firefly Algorithm ◽

Optimization Technique ◽

Particle Swarm ◽

Pso Algorithm ◽

Transient Responses ◽

Swarm Optimization ◽

Pid Controller Tuning ◽

Tank System

This paper shows the study of tuning the Proportional-Integral-Derivatives (PID) in the application of coupled tank system. The controller was tuned by using an optimization technique which is a Firefly Algorithm (FA) and a Particle Swarm Optimization (PSO) Algorithm. Both FA and PSO performance were evaluated by using performance index of Integral Time Square Error (ITSE). The systems response of FA and PSO were gathered and compared in term of transient responses, ITSE and standard deviation by considering the system condition of with and without a disturbance. The simulation is conducted by using MATLAB software. The result shows that the FA giving a better system performance compared to PSO in term of overall transient responses.

Download Full-text