Truncation of fractional derivative for online system identification

Fractional derivatives are non local operators that has compacity property in terms of parameter number for modeling diffusive phenomenon with very few parameters. One of its main properties is its non-local behavior, as it can be exploited to model long-memory phenomena such as heat transfers. However, such non-locality implies a constant knowledge of the full past of the function to be differentiated. In the context of real-time system identification, this may limit the experiences as calculations become slower as time progresses. This study deals with the relationship between frequency content of a signal and its truncation error in order to obtain real-time exploitable algorithms

Long-memory recursive prediction error method for identification of continuous-time fractional models

This paper deals with recursive continuous-time system identification using fractional-order models. Long-memory recursive prediction error method is proposed for recursive estimation of all parameters of fractional-order models. When differentiation orders are assumed known, least squares and prediction error methods, being direct extensions to fractional-order models of the classic methods used for integer-order models, are compared to our new method, the long-memory recursive prediction error method. Given the long-memory property of fractional models, Monte Carlo simulations prove the efficiency of our proposed algorithm. Then, when the differentiation orders are unknown, two-stage algorithms are necessary for both parameter and differentiation-order estimation. The performances of the new proposed recursive algorithm are studied through Monte Carlo simulations. Finally, the proposed algorithm is validated on a biological example where heat transfers in lungs are modeled by using thermal two-port network formalism with fractional models

Modeling thermal systems with fractional models: human bronchus application

System thermal modeling allows heat and temperature simulations for many applications, such as refrigeration design, heat dissipation in power electronics, melting processes and bio-heat transfers. Sufficiently accurate models are especially needed in open-heart surgery where lung thermal modeling will prevent pulmonary cell dying. For simplicity purposes, simple RC circuits are often used, but such models are too simple and lack of precision in dynamical terms. A more complete description of conductive heat transfer can be obtained from the heat equation by means of a two-port network. The analytical expressions obtained from such circuit models are complex and nonlinear in the frequency ω. This complexity in Laplace domain is difficult to handle when it comes to control applications and more specifically during surgery, as heat transfer and temperature control of a tissue may help in reducing necrosis and preserving a greater amount of a given organ. Therefore, a frequency-domain analysis of the series and shunt impedances will be presented and different techniques of approximations will be explored in order to obtain simple but sufficiently precise linear fractional transfer function models. Several approximations are proposed to model heat transfers of a human middle bronchus and will be quantified by the absolute errors

Global Thermal Modeling of Lung Heat Transfer with Blood Perfusion

Thermal models often consider low-frequency approximations and are largely based upon RC circuits. If temperature fluctuations are non-negligible, a more accurate model is needed and is proposed through thermal two-port network which is direct extension of the heat equation into matrix formalism. As blood flow plays a major role for heat transfers in biological tissue, a second model is obtained from the bio-heat equation. These two type of models are combined into a global thermal modeling of human lung by considering blood perfusion