dc dc buck converter for Model predictive control

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

[Chandrakanth Pavanaskar]

Hello,

I just saw your model, can I know how did you get the solution as the solution seems infeasible

[Sam Chak]

@Chandrakanth Pavanaskar

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

Answer 1

[Chandrakanth Pavanaskar]

Hello,

Can I know the research material for this MPC design of buck converter?

Thank you