how to define mpc object's plant as state space ?

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AMAN
AMAN 2023-10-19
评论： Sam Chak 2023-10-19
how to define the mpc object's plant as state space rather than transfer function. i tried to run this code but not working.
% Define system matrices (Ad, Bd, Cd, Dd) for the quadruple tank system
Ad = [-0.0173190, 0, 0.026219, 0; 0, -0.0113455, 0, 0.017708; 0, 0, -0.026219, 0; 0, 0, 0, -0.017708];
Bd = [0.0395, 0; 0, 0.03598; 0, 0.076375; 0.06378, 0];
Cd = [1, 0, 0, 0; 0, 1, 0, 0];
Dd = [0, 0; 0, 0];
% Define prediction and control horizons
predictionHorizon = 10; % Adjust as needed
controlHorizon = 3; % Adjust as needed
% Define constraints (input and state constraints)
inputConstraints = [-10, 10; -10, 10]; % Adjust as needed
stateConstraints = [0, 40; 0, 40; 0, 40; 0, 40]; % Adjust as needed
% Define cost function weights
Q = eye(4); % State weight matrix (adjust as needed)
R = eye(2); % Input weight matrix (adjust as needed)
% Initial state
x0 = [10; 10; 10; 10]; % Adjust the initial state as needed
% MPC setup
mpcobj = mpc(Ad, Bd, Cd, Dd, 'PredictionHorizon', predictionHorizon, 'ControlHorizon', controlHorizon);

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Sam Chak
Sam Chak 2023-10-19
Ran in:
Hi @AMAN
There was an incorrect syntax issue with mpc(), but it is now fixed below:
% Define system matrices (Ad, Bd, Cd, Dd) for the quadruple tank system
Ad = [-0.0173190, 0, 0.026219, 0; 0, -0.0113455, 0, 0.017708; 0, 0, -0.026219, 0; 0, 0, 0, -0.017708];
Bd = [0.0395, 0; 0, 0.03598; 0, 0.076375; 0.06378, 0];
Cd = [1, 0, 0, 0; 0, 1, 0, 0];
Dd = [0, 0; 0, 0];
sys = ss(Ad, Bd, Cd, Dd) % <-- added this
sys = A = x1 x2 x3 x4 x1 -0.01732 0 0.02622 0 x2 0 -0.01135 0 0.01771 x3 0 0 -0.02622 0 x4 0 0 0 -0.01771 B = u1 u2 x1 0.0395 0 x2 0 0.03598 x3 0 0.07637 x4 0.06378 0 C = x1 x2 x3 x4 y1 1 0 0 0 y2 0 1 0 0 D = u1 u2 y1 0 0 y2 0 0 Continuous-time state-space model.
% Define prediction and control horizons
predictionHorizon = 10; % Adjust as needed
controlHorizon = 3; % Adjust as needed
% Define constraints (input and state constraints)
inputConstraints = [-10, 10; -10, 10]; % Adjust as needed
stateConstraints = [0, 40; 0, 40; 0, 40; 0, 40]; % Adjust as needed
% Define cost function weights
Q = eye(4); % State weight matrix (adjust as needed)
R = eye(2); % Input weight matrix (adjust as needed)
% Initial state
x0 = [10; 10; 10; 10]; % Adjust the initial state as needed
% MPC setup
ts = 0.1; % <-- added this
mpcobj = mpc(sys, ts, predictionHorizon, controlHorizon) % <-- fixed this
-->"Weights.ManipulatedVariables" is empty. Assuming default 0.00000. -->"Weights.ManipulatedVariablesRate" is empty. Assuming default 0.10000. -->"Weights.OutputVariables" is empty. Assuming default 1.00000. MPC object (created on 19-Oct-2023 17:07:26): --------------------------------------------- Sampling time: 0.1 (seconds) Prediction Horizon: 10 Control Horizon: 3 Plant Model: -------------- 2 manipulated variable(s) -->| 4 states | | |--> 2 measured output(s) 0 measured disturbance(s) -->| 2 inputs | | |--> 0 unmeasured output(s) 0 unmeasured disturbance(s) -->| 2 outputs | -------------- Disturbance and Noise Models: Output disturbance model: default (type "getoutdist(mpcobj)" for details) Measurement noise model: default (unity gain after scaling) Weights: ManipulatedVariables: [0 0] ManipulatedVariablesRate: [0.1000 0.1000] OutputVariables: [1 1] ECR: 100000 State Estimation: Default Kalman Filter (type "getEstimator(mpcobj)" for details) Unconstrained Use built-in "active-set" QP solver with MaxIterations of 120.

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