Design of a linear quadratic regulator for nonlinear systems modeled via norm-bounded linear differential inclusions

Rodrigues, Carolina Ribeiro; Kuiava, Rôman; Ramos, Rodrigo A.

doi:10.3182/20110828-6-it-1002.01207

Cited by 8 publications

(1 citation statement)

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“…In this paper a quadratic cost index with the same goal herein discussed is proposed; the adopted SIRC model is nonlinear for the presence of the interaction terms. The case of nonlinear dynamical system with a quadratic cost index is rather common and inspired different solutions, depending on the specific model and on the nonlinearity, [22], [23], [24]. In [24] this problem is faced referring to a musculo-skeletal arm discrete model; the authors developed an iterative linear quadratic regulator algorithm based on an iterative linearization of the nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

A linear quadratic regulator for nonlinear SIRC epidemic model

Giamberardino

Iacoviello

2019

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)

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The control of an epidemic disease consists in introducing the strategies able to reduce the number of infected subjects by means of medication/quarantine actions, and the number of the subjects that could catch the disease through an informative campaign and, when available, a vaccination strategy. Some diseases, like the influenza, do not guarantee immunity; therefore, the subjects could get ill again by different strain of the same viral subtype. The epidemic model adopted in this paper introduces the cross-immune individuals; it is known in literature as SIRC model, since the classes of susceptible (S), infected (I), removed (R) and cross-immune (C) subjects are considered. Its control is herein determined in the framework of the linear quadratic regulator, by applying to the original nonlinear model the optimal control found on the linearized system. The results appear satisfactory, and the drawback of using a control law based on the linear approximation of the system is compensated by the advantages arising from such a solution: no costate equations to be solved and a solution depending on the current state evolution which allows a feedback implementation.

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Section: Introductionmentioning

confidence: 99%