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Highlights from Model Predictive Control of Multi-Input, Multi-Output (MIMO) systems

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Model Predictive Control of Multi-Input, Multi-Output (MIMO) systems

07 Jun 2013 (Updated )

Quadratic Programming is used to simulate Model Predictive Control of MIMO systems

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Description

These scripts set up and simulate Model Predictive Control of a general Multi-Input, Multi-Output (MIMO) Control system, when the linearized state-space model (or transfer function) is given as input to the functions. However, the plant model can be nonlinear in general.

Quadratic programming is used to make the input and output variables reach their set-points in the desired time horizon.

The description of the files is as follows:
run_MPC.m: The main file that sets up and runs the simulation.
MPC_simulation.m: Iterates through time and implements the present time input variables that are found at each iterate.
MPC_calculation: The MPC controller that solves the quadratic problem with looking at a forward time horizon based on the linearized model of the plant.
MPC_plant.m: Implements the present time input vector in the plant. In general, the plant model can be nonlinear.
Addnoise.m: A function to add noise to the main signal (output of the plant), based on the order of magnitude of the signal, and noise percentage (noise std)

The formulation and original code (for SISO systems) is by Elling W. Jacobsen from KTH university, Sweden. The formulation is included in the files. The code is modified and generalized for MIMO systems by Pooya Rezaei, University of Vermont, USA.

MATLAB release MATLAB 7.11 (R2010b)
11 Jun 2013
14 Jun 2013

delta_u formulation (page 24 of pdf) is added (*_delta_u). The new obj func to be minimized is: f = 1/2*(y-yref)'*Qy*(y-yref)+1/2*delta_u'*Qu*delta_u
Also, the capability of having a varied yref is added.
Run run_MPC_delta_u.m.