how to solve error Cannot create variable 'e1' in workspace

erorr in pso wind

Cannot create variable 'e1' in workspace

Error in Untitledpsowind (line 34)

current_fitness(i) = simulPID(current_position(:,i));

Caused by:

Error using Untitledpsowind>simulPID (line 143)

Attempt to add "e1" to a static workspace .

See Variables in Nested and Anonymous Functions.

code

%         Tuning of PID controller using Particle Swarm Optimization    %
%Initialization
 
clear
clc
n = 5          % Size of the swarm " no of birds "
bird_setp  =20   % Maximum number of "birds steps"
dim = 6         % Dimension of the problemmk
 
c1 = 1.2 ;         % PSO parameter C1 
c2 = 1.2  ;        % PSO parameter C2 
w  = 0.8   ;     % pso momentum   
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 = 5*(rand (dim,n));
velocity = 0.3*randn(dim, n);
local_best_position  = current_position ;
 
 
                   %-------------------------------------------%
                   %     Evaluate initial population           %           
                   %-------------------------------------------%
for i = 1:n
    current_fitness(i) = simulPID(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 )
    
R1 = rand(dim, n) ;
R2 = rand(dim, n) ;
iter = iter + 1 ;
 
for i = 1:n,
[current_fitness(i)] = simulPID(current_position(:,i)) ;
%out(:,i)=j;
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) ;
  
 % outt(iter,:)=out(:,g);  
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=local_best_position;
 
 current_position = current_position + velocity; 
 
 
 
 
  
sprintf('Iteration No. = %3.0f , Fitness =%3.7f', iter,global_best_fitness )
sprintf('The value of Kp = %3.4f, Ki = %3.4f', globl_best_position(1),globl_best_position(2), globl_best_position(3),globl_best_position(4), globl_best_position(5),globl_best_position(6)) 
resul(:,iter)=globl_best_position(:,1);
end % end of while loop its mean the end of all step that the birds move it 
% ss=outt';
 sprintf('pitch angle control gain G1: [Kp Ki]') 
 Kp=resul(1,iter)
 Ki=resul(2,iter)
 sprintf('pitch angle control gain G2: [Kp Ki]')
 Kp=resul(3,iter)
 Ki=resul(4,iter)
 sprintf('pitch angle control gain G3: [Kp Ki]')
 Kp=resul(5,iter)
 Ki=resul(6,iter)
 
 
                                               %------------------------%
                                               %          plot          %
                                               %------------------------%
 
  plot(resul,'DisplayName','resul','YDataSource','resul');figure(gcf)
 
 
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
The PSO gain tuning algorithm subroutine in MATLAB illustrated below
 
% The name of subroutine is simulPI %
 
function [Y, X] = simulPID(pid)
         
         Kp = pid(1);
         Ki = pid(2);
         Kp = pid(3);
         Ki = pid(4);
         Kp = pid(5);
         Ki = pid(6);
       
      
         
         [~,~,yout] = sim('power_wind_scig_ahmed',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         Y=e1;
         X=yout; 
         
     function [Y, X] = simulPID(pid)
         
   
       
          [~,~,yout] = sim('power_wind_scig_ahmed',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         
         Y=e2;
         X=yout; 
         
     function [Y, X] = simulPID(pid)
         
     
   
       
           [~,~,yout] = sim('power_wind_scig_ahmed',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         Y=e3;
         X=yout; 
     
 
 
 
 %         Tuning of PID controller using Particle Swarm Optimization    %
Initialization
 
clear
clc
n = 5          % Size of the swarm " no of birds "
bird_setp  =20   % Maximum number of "birds steps"
dim = 6         % Dimension of the problemmk
 
c1 = 1.2 ;         % PSO parameter C1 
c2 = 1.2  ;        % PSO parameter C2 
w  = 0.8   ;     % pso momentum   
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 = 5*(rand (dim,n));
velocity = 0.3*randn(dim, n);
local_best_position  = current_position ;
 
 
                   %-------------------------------------------%
                   %     Evaluate initial population           %           
                   %-------------------------------------------%
for i = 1:n
    current_fitness(i) = simulPID(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 )
    
R1 = rand(dim, n) ;
R2 = rand(dim, n) ;
iter = iter + 1 ;
 
for i = 1:n,
[current_fitness(i)] = simulPID(current_position(:,i)) ;
%out(:,i)=j;
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) ;
  
 % outt(iter,:)=out(:,g);  
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=local_best_position;
 
 current_position = current_position + velocity; 
 
 
 
 
  
sprintf('Iteration No. = %3.0f , Fitness =%3.7f', iter,global_best_fitness )
sprintf('The value of Kp = %3.4f, Ki = %3.4f', globl_best_position(1),globl_best_position(2), globl_best_position(3),globl_best_position(4), globl_best_position(5),globl_best_position(6)) 
resul(:,iter)=globl_best_position(:,1);
end % end of while loop its mean the end of all step that the birds move it 
% ss=outt';
 sprintf('pitch angle control gain G1: [Kp Ki]') 
 Kp=resul(1,iter)
 Ki=resul(2,iter)
 sprintf('pitch angle control gain G2: [Kp Ki]')
 Kp=resul(3,iter)
 Ki=resul(4,iter)
 sprintf('pitch angle control gain G3: [Kp Ki]')
 Kp=resul(5,iter)
 Ki=resul(6,iter)
 
 
                                               %------------------------%
                                               %          plot          %
                                               %------------------------%
 
  plot(resul,'DisplayName','resul','YDataSource','resul');figure(gcf)
 
 
 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
The PSO gain tuning algorithm subroutine in MATLAB illustrated below
 
% The name of subroutine is simulPI %
 
function [Y, X] = simulPID(pid)
         
         Kp = pid(1);
         Ki = pid(2);
         Kp = pid(3);
         Ki = pid(4);
         Kp = pid(5);
         Ki = pid(6);
       
      
         
         [~,~,yout] = sim('untitledPSO',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         Y=e1;
         X=yout; 
         
     function [Y, X] = simulPID(pid)
         
   
       
          [~,~,yout] = sim('untitledPSO',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         
         Y=e2;
         X=yout; 
         
     function [Y, X] = simulPID(pid)
         
     
   
       
           [~,~,yout] = sim('untitledPSO',[0 15], simset('solver','ode23tb','AbsTol',1e-6,'SrcWorkspace','Current','DstWorkspace','Current')); 
         
         Y=e3;
         X=yout; 
     

Answer 1

[Walter Roberson]

Before each call to sim(), assign some value (any value) to each variable that your model outputs -- which appears to be at least e1, e2, and e3 .

Note: you cannot have more than one function with the same name.

[Mustafa Sabah]

not work my project file link

https://drive.google.com/file/d/11L5tVhjt9xp08VZ21qF6pFyrowKZfQhE/view?usp=drivesdk

[Walter Roberson]

No permission to access that.

[Walter Roberson]

Please attach your .m files

[Mustafa Sabah]

this method pi tuning pso by use ITSE(integral time square error)

my code m.file link

https://drive.google.com/file/d/1A9Sqz2iuxyXIzat8EpXcQDOTI8v_7W56/view?usp=drivesdk

[Mustafa Sabah]

[Walter Roberson]

Vnom and Pnom are undefined in your model.

You did not make the change I indicated to initialize e1, e2, e3 to something before calling sim()

Why do you have three different functions with the same name, that run the same code, but just return different e1, e2, e3? If you need all three of those why not just return all three?

[Mustafa Sabah]

Because of the different data within each turbine 1, 2 and 3 They represent e1, e2, e3.

If you run a project only You will notice that the data in the workspace e1 e2 e3 is different

If you use one function, another error appears

error

Unable to perform assignment because the left and right sides have a different number of elements.

Error in

current_fitness(i) = simulPID(current_position(:,i));

[Walter Roberson]

Two of your three functions cannot be called.

Do you want to optimize all three turbines simultaneously with one set of parameters? Do you want to optimize them independently?

If you want to optimize them simultaneously, then what tradeoff are you willing to accept? Minimize the total of the three? Minimize the differences (looking for equal values) ? If one particular parameter lowers e1 by 2 but increases e2 by 1, should that parameter be accepted?

I still need Vnom and Pnom values in order to be able to run your code.