Adaptive PID Controller For DC Motor Speed Control

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Mnh
Mnh 2025-10-11，17:28
评论： Umar 16 minutes 前
I am currently working on a project titled “Speed Control of a DC Motor Using an Adaptive PID Controller.” However, during the control process, I have encountered several issues that cause the motor to operate unstably. Specifically, the plant does not closely follow the reference model at low and medium speed ranges. Therefore, I would like to ask if anyone has a Simulink diagram related to this topic that I could refer to.
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Mnh
Mnh 39 minutes 前
编辑：Mnh 30 minutes 前
%% MRAS Adaptive PID (MIT rule) for DC Motor (Realtime Plot)
% Hardware:
% - Arduino UNO R3 + L298N
% - Encoder: 334 pulses/rev × 34 (gear ratio)
% - No-load: ~160 rpm ≈ 16.76 rad/s
% - Supply: 12V
clc; clear; close all;
%% --- Arduino setup ---
a = arduino('COM4','Uno','Libraries',{'RotaryEncoder'});
enc = rotaryEncoder(a,'D2','D3',334*34); % TOTAL_PPR = 11356
in1 = 'D4'; in2 = 'D5'; ena = 'D6';
configurePin(a,in1,'DigitalOutput');
configurePin(a,in2,'DigitalOutput');
configurePin(a,ena,'PWM');
%% --- Parameters ---
T = 0.03; % Sample time [s]
am = 6.0; bm = 6.0; % Reference model
ym = 0; % Model output
% MRAS learning parameters
gamma_p = 0.010; gamma_i = 0.003; gamma_d = 0.002;
norm_p = 0.08; norm_i = 0.02; norm_d = 0.05;
% PID gains and bounds
Kp = 1.8; Ki = 0.7; Kd = 0.05;
Kp_min=0.2; Kp_max=6.0; Ki_min=0; Ki_max=2.5; Kd_min=0; Kd_max=0.2;
% Filters
speed_alpha = 0.6; deriv_alpha = 0.5;
MAX_SPEED = 31; % rad/s
PWM_MAX = 255; PWM_DEADZONE = 8;
% States
I = 0; e_prev = 0; yp_filt = 0; d_filt = 0;
count_prev = 0;
setpoint = 5; % rad/s
disp('MRAS Adaptive PID with realtime plot. Use Ctrl+C to stop.');
fprintf('Current setpoint: %.2f rad/s\n', setpoint);
%% --- Initialize Realtime Plot ---
figure('Name','MRAS Adaptive PID Realtime','NumberTitle','off','Color','w');
subplot(2,1,1);
hRef = animatedline('Color',[0.7 0.7 0.7],'LineWidth',1.5,'LineStyle','--');
hYm = animatedline('Color',[0.2 0.6 1],'LineWidth',1.8);
hYp = animatedline('Color',[1 0.3 0.2],'LineWidth',1.8);
xlabel('Time (s)'); ylabel('Speed (rad/s)');
title('Motor Speed Tracking (Y_p vs Y_m)');
legend({'Ref','Model (Y_m)','Plant (Y_p)'},'Location','best');
grid on;
subplot(2,1,2);
hKp = animatedline('Color','r','LineWidth',1.5);
hKi = animatedline('Color','b','LineWidth',1.5);
hKd = animatedline('Color','g','LineWidth',1.5);
xlabel('Time (s)'); ylabel('Gain value');
title('Adaptive PID Gains (Kp, Ki, Kd)');
legend({'Kp','Ki','Kd'},'Location','best');
grid on;
%% --- Main Control Loop ---
t0 = tic;
while ishandle(hRef)
t = toc(t0);
% --- (1) Measure speed ---
count = readCount(enc);
delta = count - count_prev;
count_prev = count;
rev = double(delta)/11356;
inst_speed = rev * 2*pi / T;
if abs(inst_speed) > MAX_SPEED && abs(inst_speed) > 3*abs(yp_filt + 1e-6)
inst_speed = yp_filt;
end
yp_filt = speed_alpha*yp_filt + (1-speed_alpha)*inst_speed;
yp = yp_filt;
% --- (2) Reference model ---
ym = ym + T*(-am*ym + bm*setpoint);
% --- (3) Error terms ---
e = setpoint - yp;
em = ym - yp;
% --- (4) Derivative & Integral ---
d_raw = (e - e_prev)/T;
d_filt = deriv_alpha*d_filt + (1-deriv_alpha)*d_raw;
I = I + e*T;
% --- (5) Control law ---
uP = Kp*e + Kd*d_filt;
u_tent = uP + Ki*I;
if (u_tent>=PWM_MAX && e>0) || (u_tent<=0 && e<0)
I = I*0.999;
end
u = uP + Ki*I;
pwmOut = min(max(u,0),PWM_MAX);
if pwmOut>0 && pwmOut<PWM_DEADZONE
pwmOut = PWM_DEADZONE;
end
writeDigitalPin(a,in1,1);
writeDigitalPin(a,in2,0);
writePWMVoltage(a,ena,5.0*pwmOut/255);
% --- (6) MIT adaptation ---
freeze = ( (pwmOut>=PWM_MAX && em>0) || (pwmOut<=0 && em<0) );
if ~freeze
phi_p = e; phi_i = I; phi_d = d_filt;
dKp = gamma_p * em * phi_p / (1 + norm_p*phi_p^2);
dKi = gamma_i * em * phi_i / (1 + norm_i*phi_i^2);
dKd = gamma_d * em * phi_d / (1 + norm_d*phi_d^2);
Kp = Kp + dKp; Ki = Ki + dKi; Kd = Kd + dKd;
Kp = min(max(Kp,Kp_min),Kp_max);
Ki = min(max(Ki,Ki_min),Ki_max);
Kd = min(max(Kd,Kd_min),Kd_max);
end
% --- (7) Realtime plot update ---
addpoints(hRef,t,setpoint);
addpoints(hYm,t,ym);
addpoints(hYp,t,yp);
addpoints(hKp,t,Kp);
addpoints(hKi,t,Ki);
addpoints(hKd,t,Kd);
drawnow limitrate;
% --- (8) Display info every ~0.3s ---
if mod(round(t,2),0.3)<T
fprintf('t=%.1fs | Yp=%.2f | Ym=%.2f | e=%.2f | Kp=%.2f Ki=%.2f Kd=%.2f | PWM=%3d\n',...
t, yp, ym, em, Kp, Ki, Kd, round(pwmOut));
end
e_prev = e;
pause(T);
end
disp('Stopped MRAS Adaptive PID realtime loop.');
Hello @Umar, thank you for your reminder. I am currently controlling a motor using a MATLAB .m file. However, when I upload the code to the motor for execution, I encounter several errors that I have been unable to resolve. I am considering switching to Simulink for implementation, but if possible, could you please guide me on how to fix these errors in the MATLAB .m code?
Umar
Umar 16 minutes 前

Hi @Mnh,

This will take some time to figure out the root cause. Let me review it carefully and I will get back to you. Hope you are not in rush.

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回答（1 个）

Sabin
Sabin about 6 hours 前
Without a sample model we can not really point to any specific problem. You can start building the simulation model using existing examples in Simscape Electrical. This example shows a cascade speed-control structure for a DC motor:
https://www.mathworks.com/help/sps/ug/dc-motor-control.html
Simscape Electrical also provide a discrete-time PID-based model reference adaptive control that can be used in the example I mentionded:
https://www.mathworks.com/help/sps/ref/modelreferenceadaptivecontroller.html

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