# Tunning of PID controller using Particle Swarm Optimization

### wael korani (view profile)

11 Jun 2008 (Updated )

tunining of PID controller by using PSO

Tunning of PID controller using Particle Swarm Optimization

# Tunning of PID controller using Particle Swarm Optimization

Author: Wael Mansour (wael192@yahoo.com)

MSc Student, Electrical Enginering Dept, Faculty of Engineering Cairo University, Egypt

## Initialization

```clear
clc
n = 50;           % Size of the swarm " no of birds "
bird_setp  =50;   % Maximum number of "birds steps"
dim = 2;          % Dimension of the problem

c2 =1.2;          % PSO parameter C1
c1 = 0.12;        % PSO parameter C2
w =0.9;           % pso momentum or inertia
fitness=0*ones(n,bird_setp);

%-----------------------------%
%    initialize the parameter %
%-----------------------------%

R1 = rand(dim, n);
R2 = rand(dim, n);
current_fitness =0*ones(n,1);

%------------------------------------------------%
% Initializing swarm and velocities and position %
%------------------------------------------------%

current_position = 10*(rand(dim, n)-.5);
velocity = .3*randn(dim, n) ;
local_best_position  = current_position ;

%-------------------------------------------%
%     Evaluate initial population           %
%-------------------------------------------%

for i = 1:n
current_fitness(i) = tracklsq(current_position(:,i));
end

local_best_fitness  = current_fitness ;
[global_best_fitness,g] = min(local_best_fitness) ;

for i=1:n
globl_best_position(:,i) = local_best_position(:,g) ;
end
%-------------------%
%  VELOCITY UPDATE  %
%-------------------%

velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));

%------------------%
%   SWARMUPDATE    %
%------------------%

current_position = current_position + velocity ;

%------------------------%
%  evaluate anew swarm   %
%------------------------%
```

## Main Loop

```iter = 0 ;        % Iterations’counter
while  ( iter < bird_setp )
iter = iter + 1;

for i = 1:n,
current_fitness(i) = tracklsq(current_position(:,i)) ;
end

for i = 1 : n
if current_fitness(i) < local_best_fitness(i)
local_best_fitness(i)  = current_fitness(i);
local_best_position(:,i) = current_position(:,i)   ;
end
end

[current_global_best_fitness,g] = min(local_best_fitness);

if current_global_best_fitness < global_best_fitness
global_best_fitness = current_global_best_fitness;

for i=1:n
globl_best_position(:,i) = local_best_position(:,g);
end

end

velocity = w *velocity + c1*(R1.*(local_best_position-current_position)) + c2*(R2.*(globl_best_position-current_position));
current_position = current_position + velocity;

sprintf('The value of interation iter %3.0f ', iter );

end % end of while loop its mean the end of all step that the birds move it

xx=fitness(:,50);
[Y,I] = min(xx);
current_position(:,I)

%
```
```ans =

0.9399
0.5610

```