“…A rational transfer functioñ( ) is primitive if the maximum modulus pole of̃( ) is the only maximum real eigenvalue of ( + ), which is similar to concepts presented in discrete-time versions [5,20]. Thus, a transfer function ( ) is defined by a primitive transfer function, if̃( ) is a primitive transfer function for some .…”
Section: Lemma 3 Assume That̃( ) Is Decomposed Intõmentioning
This paper discusses a computational method to construct positive realizations with sparse matrices for continuous-time positive linear systems with multiple complex poles. To construct a positive realization of a continuous-time system, we use a Markov sequence similar to the impulse response sequence that is used in the discrete-time case. The existence of the proposed positive realization can be analyzed with the concept of a polyhedral convex cone. We provide a constructive algorithm to compute positive realizations with sparse matrices of some positive systems under certain conditions. A sufficient condition for the existence of a positive realization, under which the proposed constructive algorithm works well, is analyzed.
“…A rational transfer functioñ( ) is primitive if the maximum modulus pole of̃( ) is the only maximum real eigenvalue of ( + ), which is similar to concepts presented in discrete-time versions [5,20]. Thus, a transfer function ( ) is defined by a primitive transfer function, if̃( ) is a primitive transfer function for some .…”
Section: Lemma 3 Assume That̃( ) Is Decomposed Intõmentioning
This paper discusses a computational method to construct positive realizations with sparse matrices for continuous-time positive linear systems with multiple complex poles. To construct a positive realization of a continuous-time system, we use a Markov sequence similar to the impulse response sequence that is used in the discrete-time case. The existence of the proposed positive realization can be analyzed with the concept of a polyhedral convex cone. We provide a constructive algorithm to compute positive realizations with sparse matrices of some positive systems under certain conditions. A sufficient condition for the existence of a positive realization, under which the proposed constructive algorithm works well, is analyzed.
“…Many advanced issues regarding positive system analysis and control have been studied by a large number of authors from the '70s. Just to cite a few: controllability [3][4][5][6], stabilization [7,8], behavioural approach [9,10], optimal control [11], identification [12], realization [5,[13][14][15][16][17][18] and switched systems [19].…”
This review paper presents four relevant and very recent real-world application problems demanding developments of long-standing theoretical open problems in the field of positive systems research. Notably, the selected applications belong to very different fields of science and technology, ranging from biology and medicine to civil and electronic engineering. This clearly shows how pervasive positive systems are in mainstream research. Additionally, the theoretical issues stemming from these applications are the living proofs of how the apparently simple positivity constraint on the variables of interest makes the theory behind practical problems far from trivial, even for the linear case.
“…These problems have been given considerable attention over the past decade. The existence problem was completely solved in [2] and [9], cf., [16,17,11], while a few particular cases of the minimality problem were settled in [8,13,19,3,23,22].…”
We observe that successive applications of known results from the theory of
positive systems lead to an {\it efficient general algorithm} for positive
realizations of transfer functions. We give two examples to illustrate the
algorithm, one of which complements an earlier result of \cite{large}. Finally,
we improve a lower-bound of \cite{mn2} to indicate that the algorithm is indeed
efficient in general
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