Adaptive MPC Block Error

Question

LB_SD 2020-12-16

1
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https://ww2.mathworks.cn/matlabcentral/answers/695215-adaptive-mpc-block-error

评论： Majid Mazouchi 2023-7-17

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Question:

How can I correct the error message that shows when I run the model?

Error evaluating 'InitFcn' callback of Adaptive MPC block (mask) 'test22/Adaptive MPC Controller'. Callback string is 'ampcblock_InitFcn'
Caused by: For compatibility with the adaptive mode, the plant model specified in your controller object must be LTI state-space (OK), have the same sampling time as the controller (Violated), and be delay-free (OK). The "ss", "c2d", "d2d" and "absorbDelay" functions perform the necessary conversions.

What I Have Done:

1) I have tested the model update function independently and it works great.

2) I have tested the dynamic plant using the normal MPC block and it worked well.

Problem

Once I connect the Adaptive MPC block, the error shows up. Please see model and functions used below.

Model Update Function:

function [A,B,C,D,U,Y,X,DX] = fcn(M,x,u)
% Sample time
Ts = 0.02;
% Model parameters
kd=0.015;
R=0.05;
Tg=0.2;
% Continuous-time model
Ac= [0 1 0 0; -(kd/(M*Tg)+1/(R*M*Tg)) -(kd/M+1/Tg) 0 0; 0 0 0 0; 0 0 0 0];
Bc= [0 -1/(M*Tg) 0 0]';
Cc = [1 0 0 0; 0 1 0 0];
Dc = zeros(2,1);
% Generate discrete-time model
nx = size(Ac,1);
nu = size(Bc,2);
Md = expm([[Ac Bc]*Ts; zeros(nu,nx+nu)]);
A = Md(1:nx,1:nx);
B = Md(1:nx,nx+1:nx+nu);
C = Cc;
D = Dc;
% Nominal conditions for discrete-time plant
X = x;
U = u;
Y = C*x + D*u;
DX = A*x+B*u-x;

Dynamic Plant Function:

function xdot = fcn(M,x,u)
% Model parameters
kd=0.015;
R=0.05;
Tg=0.2;
% Continuous-time model
A= [0 1 0 0; -(kd/(M*Tg)+1/(R*M*Tg)) -(kd/M+1/Tg) 0 0; 0 0 0 0; 0 0 0 0];
B= [0 -1/(M*Tg) 0 0]';
C = [1 0 0 0; 0 1 0 0];
D = zeros(2,1);
xdot = A*x + B*u;

MPC Object

MPC object 
---------------------------------------------
Sampling time:      0.02 (seconds)
Prediction Horizon: 10
Control Horizon:    1
Plant Model:        
                                      --------------
      1  manipulated variable(s)   -->|  4 states  |
                                      |            |-->  2 measured output(s)
      0  measured disturbance(s)   -->|  1 inputs  |
                                      |            |-->  0 unmeasured output(s)
      0  unmeasured disturbance(s) -->|  2 outputs |
                                      --------------
Disturbance and Noise Models:
        Output disturbance model: default (type "getoutdist(mpcVSG4by4)" for details)
         Measurement noise model: default (unity gain after scaling)
Weights:
        ManipulatedVariables: 0
    ManipulatedVariablesRate: 0.0595
             OutputVariables: [5.9452 0.0595]
                         ECR: 100000
State Estimation:  Default Kalman Filter (type "getEstimator(mpcVSG4by4)" for details)
Constraints:
 -22 <= delP <= 22, -10 <= delP/rate <= 10, -311.02 <= State-Space(1) <= 345.58
                                                      -1 <= State-Space(2) <= 1