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Model Predictive Control

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266 12 Constrained Robust Optimal <strong>Control</strong><br />

Then, for all x ∈ X0, limk→∞ d(x(k), O) = 0. X0 is called the region of attraction.<br />

Remark 12.10 Theorem 12.7 holds also for the CROC-OL (12.41)–(12.45). The<br />

region of attraction will be X OL<br />

0 .<br />

Remark 12.11 Assumptions A4 and A5 in Theorem 12.7 imply that stage cost q(x,u)<br />

and terminal cost p(x) in (12.41) need to be zero in O. Note that this cannot be<br />

obtained with p = 2 in (12.41).<br />

Remark 12.12 Robust control invariance of the set Xf in Assumption (A2) of Theorem<br />

12.7 implies that problem (12.107) in Assumption (A3) is always feasible.<br />

Remark 12.13 Robust control invariance of the set O in Assumption (A2) of Theorem<br />

12.7 is also implicitly guaranteed by Assumption (A3) as shown next.<br />

From Remark 12.11, Assumption (A3) in O becomes minu maxw a ,w p(p(A(wp )x +<br />

B(w p )u + Ew a ) ≤ 0 for all x ∈ O. From Assumption (A4), this can be verified only<br />

if it is equal to zero, i.e. if it exists a u ∈ U such that A(w p )x + B(w p )u + Ew a ∈ O<br />

for all w a ∈ W a w p ∈ W p (since O is the only place where p(x) can be zero). This<br />

implies the robust control invariance of O.<br />

12.9 Literature Review<br />

An extensive treatment of robust invariant sets can be found in [50, 51, 48, 52].<br />

The proof to Theorem 12.1 can be fond in [152, 85] For the derivation of the algorithms<br />

12.1, 12.2 for computing robust invariant sets (and their finite termination)<br />

see [10, 44, 152, 111, 147].<br />

Min-max robust constrained optimal control was originally proposed by Witsenhausen<br />

[255]. In the context of robust MPC, the problem was tackled by Campo<br />

and Morari [67], and further developed in [5] for SISO FIR plants. Kothare et<br />

al. [158] optimize robust performance for polytopic/multi-model and linear fractional<br />

uncertainty, Scokaert and Mayne [224] for additive disturbances, and Lee and<br />

Yu [165] for linear time-varying and time-invariant state-space models depending<br />

on a vector of parameters θ ∈ Θ, where Θ is either an ellipsoid or a polyhedron.<br />

Other suboptimal CROC-Cl strategies have been proposed in [158, 25, 159]. For<br />

stability and feasibility of the robust RHC (12.38), (12.104) we refer the reader<br />

to [37, 173, 181, 21].

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