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Can you turn off/on manipulated variables at specific conditions in a MPC Controller?

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Hello,
im designing an adaptive MPC Controller with two manipulated variables (MV). One of them is electric power (0-2kW) and the other one is air mass flow (0-80kg/h).
Now i need to turn off/on the second MV (air mass flow) at specific conditions. If its off, then it should be =0 and if its on, then the MPC should manipulate it within the constraints. The electric power should be manipulated the whole time.
In Simulink you can imprint the MV constraints / MV targets at specific points externally on the MPC block. For example you can say that the upper contraint should change between 0 kg/h and 80 kg/h. That works, but i feel like its not the right way.
Is there an other solution to control the MV "direct inside" the MPC, so that i can decide whether it should be manipulated or not?
Thanks
  4 Commenti
Sam Chak
Sam Chak il 17 Lug 2022
Thanks for your clarification, but I still don't know your system works mathematically. I think that MPC considers the input is continuous. If the engine is the state x, then you can probably do something like this:
... system that is fed into the MPC
where
is the ON/OFF switching logic is passed on the system dynamics side.
Then, your MPC can freely compute the input u without the internal constraint.
Artem Wendler
Artem Wendler il 19 Lug 2022
Hi,
i have a plant model, which the adaptive mpc is controlling. For the mpc i linearized my plant model and made a state space model (ss) with a script. Further the adaptive mpc needs a updater, which updates the ss model continuously. In the last step i need to discretize my updated ss model and feed it to the mpc.
Thank you for your suggestion, but what do you mean by B(x) is ON/OFF since B is the jacobian matrix B=jacobian(f,u)? f are the derivative equations (in my case the derivative of the mass temperatures) and u are the inputs (electric power, air mass flow,...).
Thanks

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Gianluca Mammone
Gianluca Mammone il 11 Dic 2022
Modificato: Gianluca Mammone il 11 Dic 2022
Hi, I am in a similar situations as yours.
My solution was that given the linear system:
that can be written in the from and ->
if i want to disable u1, i.e having I just pass to the Adaptive MPC the model with , thus the optimal value for u1 is
Although this is a good idea, in practice Adaptive MC keeps optimizing u1 and finding nonzero optimum, I suggest to set an hard dynamic constraint on u1= 0
I'm open to better solutions tho, setting is the most obvius approach this situation, and the fact that MPC keeps resisting logic baffles me.
EDIT:
I just tried right now, you have to set mixed input/output constraint, In my example if we want to disable
&& -> E = [1 ,0; -1, 0] F= [0;0] G=[0;0] V=[0;0].
  1. bear in mind that adaptive MPC requires E,F,G,V etc of fixed dimensions, you can't add constraints (i.e rows) during the simulation.
  2. given point 1, you might be tempted to inizialize your mpc object with constraints set to zero (E =zeros(2,2), F= [0;0] G=[0;0] V=[0;0]) to enable every Manipulated Variable. Doing so Matlab automatically deletes those empty constraints and you'll find yourself unable to add it later as online feature. Therefore, you have to initialize the constraint with every MV you plan to block already blocked (E = [1 ,0; -1, 0] ), and in your simulations ovverride it.
I hope I helped you.
Best Regards

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