# dc dc buck converter for Model predictive control

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Mohamed on 29 Aug 2019
I have designed a dc dc buck converter for the Model predictive control, but when I run the code give the right numrical output but hte graphs are not acuret, any budy can help me to fix this problem
thank you
##### 2 CommentsShow NoneHide None
Chandrakanth Pavanaskar on 13 Jul 2022
Hello,
I just saw your model, can I know how did you get the solution as the solution seems infeasible
Sam Chak on 13 Jul 2022
I'm unsure, but from the mathematical perspective, the system looks 'manipulatable' with numbers without using MPC. What is the purpose of having the MPC in this case?
Vg = 15; D = 0.4; L = 2e-3; C = 10e-6; R = 100;
a = [0 -(1-D)/L; (1-D)/C -1/(R*C)];
b = [1/L 0; 0 -1/C];
c = eye(length(a));
d = [0];
sys = ss(a, b, c, d); % System in state-space model
k1 = 1;
k2 = 1;
k = [-k1/500 3/5; 0.6 (k2 - 1e3)/1e5] % Gain Matrix
k = 2×2
-0.0020 0.6000 0.6000 -0.0100
br = diag([k1/500, -k2/1e5])*b; % Rescaled Reference Input Matrix
CLsys = ss(a+b*k, br, c, d) % Closed-loop system
CLsys = A = x1 x2 x1 -1 0 x2 7.276e-12 -1 B = u1 u2 x1 1 0 x2 0 1 C = x1 x2 y1 1 0 y2 0 1 D = u1 u2 y1 0 0 y2 0 0 Continuous-time state-space model.
step(CLsys) % Closed-loop step response

Chandrakanth Pavanaskar on 14 Jul 2022
Hello,
Can I know the research material for this MPC design of buck converter?
Thank you

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