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Plot Performance

This cell demonstrates the controllers setpoint tracking performance using the parameters found from running the genetic algorithm below

% Initialize Blackbox system

close all

blackBox

temp = get(0,'showHiddenHandles');

set(0,'showHiddenHandles','on');

hfig = gcf;

handles = guidata(hfig);

set(handles.radioFile, 'Value', 1);

set(handles.inputFile, 'String', 'stepInput');

% Configure start time, end time, step size, refine output

endTime = '60'; %100

stepSize = '0.1';%'0.5'

set(handles.axisStart, 'String', '0');

set(handles.axisEnd, 'String', endTime);

set(handles.stepSize, 'String', stepSize);

set(handles.refineOutput, 'String', '1');

% Assign PID Coefficients

assignin('base','Kp',-9.839119e-02);

assignin('base','Ki',-3.393445e-01);

assignin('base','Kd',3.752320e-03);

assignin('base','tau_f',0.3);

%Run SIM

blackBox('run_Callback',handles.run,[],handles);

ans = 'starting'

ans = 'Done Runnin''

sim_t = handles.axes2.Children.XData;

sim_y = handles.axes2.Children.YData;

% sim_r = interpolate_sim_r(handles.axes1.Children.YData,sim_y);

% [rise_time, overshoot] = GetMetrics(sim_t,sim_y,sim_r)

sim_r = handles.axes1.Children.YData;

close all

figure(10)

plot(sim_t, arrayfun(@(t) stepInput(t),sim_t),sim_t, sim_y)

ylabel("output")

xlabel("time")

title("System Setpoint tracking with PID control")

set(0,'showHiddenHandles',temp);

Genetic Algorithm Run

This cell runs the genetic algorithm on the black box system and returns the optimal tuning parameters

% Initialize Blackbox system
close all
blackBox
temp = get(0,'showHiddenHandles');
set(0,'showHiddenHandles','on');
hfig = gcf;
handles = guidata(hfig);
 
% Set Input File Name
set(handles.radioFile, 'Value', 1);
set(handles.inputFile, 'String', 'stepInput');
 
 
% Configure start time,  end time, step size, refine output
endTime = '100';
stepSize = '0.5';
set(handles.axisStart, 'String', '0');
set(handles.axisEnd, 'String', endTime);
set(handles.stepSize, 'String', stepSize);
set(handles.refineOutput, 'String', '1');
 
 
% Define the boundaries for the PID parameters
fun = @(P) PIDFitness(P);
lower_bound =[-0.05 -0.7 -0.01 0];
upper_bound = [0.15 0.5 0.01 1];
 
 
% Run the genetic algorithm 
global cur_min cur_best fileID; 
cur_min = 1e100;
cur_best = [-1 -1 -1 -1];
options = optimoptions('ga','FunctionTolerance',100,'Display','final')
[bestE, MSE, exitflag,output,population,scores] = ga(fun, 4,[],[],[],[],lower_bound,upper_bound,[],options)

Helper Functions

function mse = PIDFitness(P)
    % Fitness Function for the genetic Algoirthm
    global cur_min;
    global cur_best;
    global fileID;
    break_itr = 0;
    
 
    X = fprintf('Kp: %d, Ki: %d, Kd: %d, tau_f: %d',P(1),P(2),P(3),P(4));
    disp(X);
 
    assignin('base','Kp',P(1));
    assignin('base','Ki',P(2));
    assignin('base','Kd',P(3));
    assignin('base','tau_f',P(4));
    
    hfig = findobj( 'Type', 'Figure', 'Name', 'blackBox' );
    handles = guidata(hfig);
    try
        break_itr = break_itr + 1;
        if break_itr > 5
            mse = 1e100;
        end
 
        blackBox('run_Callback',handles.run,[],handles);
        
        handles = guidata(hfig);
        sim_y = handles.axes2.Children.YData;
        sim_r = interpolate_sim_r(handles.axes1.Children.YData,sim_y);
 
    
        temp = trapz((sim_y-sim_r).^2);
 
        if temp < cur_min
            cur_min = temp;
            cur_best = [P(1) P(2) P(3) P(4)];
        end
 
        fprintf(fileID,'%f, %f, %f, %f, %f,',P(1),P(2),P(3),P(4),temp);
        disp(cur_min);
        disp(cur_best);
        mse = temp;   
 
    catch
 
        break_itr = break_itr + 1;
        if break_itr > 5
            mse=1e100;
        end
        
        fprintf(fileID,'%f, %f, %f, %f, %f,',P(1),P(2),P(3),P(4),1e100);
        display("Failed")
        mse=1e100;
    end
 
    
end
 
function [rise_time, overshoot] = GetMetrics(sim_t,sim_y,sim_r)
    %Given a simulation output, return the rise_time and overshoot
    t=4; 
    setpoint = sim_r(end);
 
    not_crossed_setpoint = 1;
    for c= 1:length(sim_y)
        if sim_y(c) > setpoint
            rise_time = sim_t(c)-4;
            not_crossed_setpoint = 0;
            break
        end
    end 
    if not_crossed_setpoint
        rise_time = 1000;
    end
    
    if max(sim_y) < setpoint
        overshoot = 1000;
    else 
        overshoot = max(sim_y) - setpoint;
    end 
end 
 
 
 
function new_sim_r = interpolate_sim_r(sim_r,sim_y)
    % Interpolate the simulation values to use the same time steps
    [step_val, step_loc] = max(sim_r);
    sim_r_scaled = ones(1,length(sim_y));
    new_step_loc = fix((step_loc/length(sim_r))*length(sim_r_scaled));
    sim_r_scaled(1,1:new_step_loc) = min(sim_r);
    sim_r_scaled(1,new_step_loc:length(sim_r_scaled)) = step_val;
    new_sim_r = sim_r_scaled;
end