An algorithm to study the nonnegativity, regularity and stability via state-feedbacks of singular systems of arbitrary index

This paper deals with singular systems of index k ≥ 1. Our main goal is to find a state-feedback such that the closed-loop system satis- fies the regularity condition and it is nonnegative and stable. In order to do that, the core-nilpotent decomposition of a square matrix is applied to the singular matrix of the system. Moreover, if the Drazin projector of this matrix is nonnegative then the previous decomposition allows us to write the core-part of the matrix in a specific block form. In addition, an algorithm to study this kind of systems via a state-feedback is designed.This paper has been partially supported by Ministry of Education of Spain [grant number DGI MTM2010-18228].Herrero Debón, A.; Francisco J. Ramírez; Thome, N. (2014). An algorithm to study the nonnegativity, regularity and stability via state-feedbacks of singular systems of arbitrary index. Linear and Multilinear Algebra. 1-11. https://doi.org/10.1080/03081087.2014.904559S11

Sign patterns that require eventual exponential nonnegativity

The matrix exponential function can be used to solve systems of linear differential equations. For certain applications, it is of interest whether or not the matrix exponential function of a given matrix becomes and remains entrywise nonnegative after some time. Such matrices are called eventually exponentially nonnegative. Often the exact numerical entries in the matrix are not known (for example due to uncertainty in experimental measurements), but the qualitative information is usually known. In this dissertation we discuss what structure on the signs of the entries of a matrix guarantees that the matrix is eventually exponentially nonnegative

Stabilization of positive linear discrete-time systems by using a Brauer's theorem

The stabilization problem of positive linear discrete-time systems (PLDS) by linear state feedback is considered. A method based on a Brauer s theorem is proposed for solving the problem. It allows us to modify some eigenvalues of the system without hanging the rest of them. The problem is studied for the single-input single-output (SISO) and for multi-input multioutput (MIMO) cases and sufficient conditions for stability and positivity of the closed-loop system are proved.The results are illustrated by numerical examples and the proposed method is used in stochastic systems.This work is supported by the Spanish DGI Grant MTM2010-18228.Cantó Colomina, B.; Cantó Colomina, R.; Kostova, S. (2014). Stabilization of positive linear discrete-time systems by using a Brauer's theorem. Scientific World Journal. 2014:1-6. https://doi.org/10.1155/2014/856356S162014Caccetta, L., & Rumchev, V. G. (2000). Annals of Operations Research, 98(1/4), 101-122. doi:10.1023/a:1019244121533Allen, L. J. S., & van den Driessche, P. (2008). The basic reproduction number in some discrete-time epidemic models. Journal of Difference Equations and Applications, 14(10-11), 1127-1147. doi:10.1080/10236190802332308Delchamps, D. F. (1988). State Space and Input-Output Linear Systems. doi:10.1007/978-1-4612-3816-4Méndez-Barrios, C.-F., Niculescu, S.-I., Chen, J., & Maya-Méndez, M. (2013). Output feedback stabilisation of single-input single-output linear systems with I/O network-induced delays. An eigenvalue-based approach. International Journal of Control, 87(2), 346-362. doi:10.1080/00207179.2013.834075Anderson, B. D. O., Ilchmann, A., & Wirth, F. R. (2013). Stabilizability of linear time-varying systems. Systems & Control Letters, 62(9), 747-755. doi:10.1016/j.sysconle.2013.05.003De Leenheer, P., & Aeyels, D. (2001). Stabilization of positive linear systems. Systems & Control Letters, 44(4), 259-271. doi:10.1016/s0167-6911(01)00146-3Fornasini, E., & Valcher, M. E. (2012). Stability and Stabilizability Criteria for Discrete-Time Positive Switched Systems. IEEE Transactions on Automatic Control, 57(5), 1208-1221. doi:10.1109/tac.2011.2173416Bru, R., Cantó, R., Soto, R. L., & Urbano, A. M. (2011). A Brauer’s theorem and related results. Central European Journal of Mathematics, 10(1), 312-321. doi:10.2478/s11533-011-0113-0Soto, R. L., & Rojo, O. (2006). Applications of a Brauer theorem in the nonnegative inverse eigenvalue problem. Linear Algebra and its Applications, 416(2-3), 844-856. doi:10.1016/j.laa.2005.12.026Silva, M. S., & de Lima, T. P. (2003). Looking for nonnegative solutions of a Leontief dynamic model. Linear Algebra and its Applications, 364, 281-316. doi:10.1016/s0024-3795(02)00569-4Mourad, B. (2013). Generalization of some results concerning eigenvalues of a certain class of matrices and some applications. Linear and Multilinear Algebra, 61(9), 1234-1243. doi:10.1080/03081087.2012.746330Pakshin, P. V., & Ugrinovskii, V. A. (2006). Stochastic problems of absolute stability. Automation and Remote Control, 67(11), 1811-1846. doi:10.1134/s0005117906110051Brauer, A. (1952). Limits for the characteristic roots of a matrix. IV: Applications to stochastic matrices. Duke Mathematical Journal, 19(1), 75-91. doi:10.1215/s0012-7094-52-01910-8Perfect, H. (1955). Methods of constructing certain stochastic matrices. II. Duke Mathematical Journal, 22(2), 305-311. doi:10.1215/s0012-7094-55-02232-8Berman, A., & Plemmons, R. J. (1994). Nonnegative Matrices in the Mathematical Sciences. doi:10.1137/1.9781611971262Cantó, B., Cardona, S. C., Coll, C., Navarro-Laboulais, J., & Sánchez, E. (2011). Dynamic optimization of a gas-liquid reactor. Journal of Mathematical Chemistry, 50(2), 381-393. doi:10.1007/s10910-011-9941-1Fieberg, J., & Ellner, S. P. (2001). Stochastic matrix models for conservation and management: a comparative review of methods. Ecology Letters, 4(3), 244-266. doi:10.1046/j.1461-0248.2001.00202.

Operator-Theoretic Characterization of Eventually Monotone Systems

Monotone systems are dynamical systems whose solutions preserve a partial order in the initial condition for all positive times. It stands to reason that some systems may preserve a partial order only after some initial transient. These systems are usually called eventually monotone. While monotone systems have a characterization in terms of their vector fields (i.e. Kamke-Muller condition), eventually monotone systems have not been characterized in such an explicit manner. In order to provide a characterization, we drew inspiration from the results for linear systems, where eventually monotone (positive) systems are studied using the spectral properties of the system (i.e. Perron-Frobenius property). In the case of nonlinear systems, this spectral characterization is not straightforward, a fact that explains why the class of eventually monotone systems has received little attention to date. In this paper, we show that a spectral characterization of nonlinear eventually monotone systems can be obtained through the Koopman operator framework. We consider a number of biologically inspired examples to illustrate the potential applicability of eventual monotonicity.Comment: 13 page