RANA PPT

PID CONTROLLER DESIGN USING GENETIC ALGORITHM

PROJECT GUIDE:Mr. VIDYA BHASKARSHUKLA

Presented By:Kunwar Pratap Singh RanaSurendra KumarDharmesh VermaKapil VermaJitendra Kumar

Project Aims And Objectives

• The aim of this project is to design a plant using Genetic Algorithm.

• The objective of this project is to show that by employing the GA method of tuning a plant, an optimization can be achieved.

Introduction

• PID controller consists of Proportional Action, Integral Action and Derivative Action. It is commonly refer to Ziegler-Nichols PID tuning parameters. It is by far the most common control algorithm

• PID controllers algorithm are mostly used in feedback loops.

What is genetic algorithm

• Genetic Algorithms (GA.s) are a stochastic global search method that mimics the process of natural evolution. It is one of the methods used for optimization

• There are three main stages of a genetic algorithm, these are known as reproduction, crossover and mutation.

• During the reproduction phase the fitness value of each chromosome is assessed. This value is used in the selection process to provide bias towards fitter individuals. Just like in natural evolution, a fit chromosome has a higher probability of being selected for reproduction.

• Once the selection process is complete, the crossover algorithm is initiated. The crossover operations swaps certain parts of the two selected strings in a bid to capture the good parts of old chromosomes and create better new ones

• GA. Mutation is the occasional random alteration of a value of a string position.

Overview

PID controller Genetic Algorithms

– A solution of reduction computation time Summary of work Conclusions

6

PID controller

G(s)F(s)Y(s)

H(s)

U(s)R(s)

D(s)

7

Controller structure

Control law

6 variables to tune

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Why Genetic Algorithms?

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Avoid your local minimum!

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Direct the GA

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10

GA optimisation problem

Penalty factors on gain and phase margins

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0 10 20 30 40 50 600

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Summary of work

A solution to speed up the GA optimisationThe two degree of freedom controller well

performs for systems such as: stable, inverse unstable, non-minimum phase, integrating long time-delay.

Model uncertainty has not been discussed.

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Conclusions

None of the proposed methods performs significantly better.

The fourth design directly penalises the impact of the high frequency measurement noise on the closed-loop system.

The GA with look up table significantly reduced the computation time.

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THANKS