New PDF release: Constrained Optimal Control of Linear and Hybrid Systems

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  • February 13, 2018
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By Francesco Borrelli

ISBN-10: 354000257X

ISBN-13: 9783540002574

ISBN-10: 3540362258

ISBN-13: 9783540362258

Many useful regulate difficulties are ruled by means of features reminiscent of kingdom, enter and operational constraints, alternations among diversified working regimes, and the interplay of continuous-time and discrete occasion platforms. at this time no technique is out there to layout controllers in a scientific demeanour for such platforms. This ebook introduces a brand new layout idea for controllers for such limited and switching dynamical platforms and results in algorithms that systematically resolve keep watch over synthesis difficulties. the 1st half is a self-contained advent to multiparametric programming, that's the most approach used to review and compute country suggestions optimum regulate legislation. The book's major goal is to derive homes of the nation suggestions answer, in addition to to acquire algorithms to compute it successfully. the focal point is on limited linear platforms and restricted linear hybrid structures. The applicability of the speculation is verified via experimental case stories: a mechanical laboratory technique and a traction regulate approach constructed together with the Ford Motor corporation in Michigan.

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Additional resources for Constrained Optimal Control of Linear and Hybrid Systems

Example text

As the number of combim! nations of constraints out of a set of m is ( m ) = (m− )! , the number of possible combinations of active constraints at the solution of a QP is at most m m m =0 ( ) = 2 . 1, Nn ≥ Nr . 1 generates critical regions. The first critical region CR0 is defined by the constraints λ(x) ≥ 0 (m constraints) and Gz(x) ≤ W + Sx (m constraints). If the strict complementary slackness condition holds, only m constraints can be active, and hence every CR is defined by m constraints.

The solution consists of 5 critical regions. 4. The reader can verify that the value function is not continuously differentiable at x = 3. 15 is not fulfilled. 4. 34). As the number of combim! nations of constraints out of a set of m is ( m ) = (m− )! , the number of possible combinations of active constraints at the solution of a QP is at most m m m =0 ( ) = 2 . 1, Nn ≥ Nr . 1 generates critical regions. The first critical region CR0 is defined by the constraints λ(x) ≥ 0 (m constraints) and Gz(x) ≤ W + Sx (m constraints).

16. 56). Then, the set K ∗ is a (possibly non-convex) polyhedral set and the value function J ∗ (·) is piecewise affine on polyhedra. If the optimizer z ∗ (x) is unique for all x ∈ K ∗ , then the optimizer function zc∗ : K ∗ → Rsc , zd∗ : K ∗ → {0, 1}sd is piecewise affine on polyhedra. Otherwise, it is always possible to define a piecewise affine optimizer function z ∗ (x) ∈ Z ∗ (θ) for all x ∈ K ∗ . Note that differently from the mp-LP, the set K ∗ can be disconnected and non-convex. 6 Multiparametric Mixed-Integer Quadratic Programming Consider the mp-QP J ∗ (x) = min z J(z, x) = z H1 z + z H2 x + xH3 x + c1 z + c2 x subj.

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Constrained Optimal Control of Linear and Hybrid Systems by Francesco Borrelli


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