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Model Predictive Control (MPC) “[…] is the only advanced control technique—that is, more advanced than standard PID control—to have had a significant and widespread impact on industrial process control” (Jan Maciejowski, Predictive control: with constraints Pearson Education Limited, 2002). The course will cover the following topics:

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Q4: How do I model multi-period inventory in AMPL, e.g., the constraints Inv[t] = Inv[t-1] + Make[t] - Demand[t]? A4: The AMPL files inventory.mod and inventory.dat in AMPL folder models a simple multiperiod (4 periods) inventory problem with the above constraint. The key is to define the inventory variable from 0 to T, but the production ...

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the constraints of a semideﬁnite program (P 1) relaxing (P) and writes them in YALMIP format to the ﬁle (3). In other words, ﬁle (3) will be created automatically once your MuPAD notebook works correctly and is evaluated. MuPAD notebooks can only be opened but cannot be called from MATLAB scripts. What we ﬁnally need is therefore a MuPAD program.

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problems via YALMIP Function-based interface, including dedicated functions for Application constraints Quality constraints Spectrum constraints Input design Objective: Find input spectrum that . minimizes experiment cost Constraint: Guarantee that application . and quality constraints on . the model are satisfied

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L. Vandenberghe EE236A (Fall 2013-14) Lecture 2 Piecewise-linear optimization • piecewise-linear minimization • ℓ 1- and ℓ∞-norm approximation • examples ...

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When YALMIP encounters a model with uncertain variables, three main steps occur. To begin with, the variables explicitly declared as uncertain are detected, and constraints involving only these variables are separated from the model. These constraints constitute (the initial) uncertainty description.