Model-order reduction of lumped parameter systems via fractional calculus
For engineers modeling complex discrete systems, this offers a new method to reduce model order without sacrificing accuracy, though it is incremental as it applies existing fractional calculus techniques to a known problem.
This paper proposes using fractional calculus to reduce the order of lumped parameter systems, achieving exact dynamic response matching under certain conditions with frequency-dependent fractional orders.
This study investigates the use of fractional order differential models to simulate the dynamic response of non-homogeneous discrete systems and to achieve efficient and accurate model order reduction. The traditional integer order approach to the simulation of non-homogeneous systems dictates the use of numerical solutions and often imposes stringent compromises between accuracy and computational performance. Fractional calculus provides an alternative approach where complex dynamical systems can be modeled with compact fractional equations that not only can still guarantee analytical solutions, but can also enable high levels of order reduction without compromising on accuracy. Different approaches are explored in order to transform the integer order model into a reduced order fractional model able to match the dynamic response of the initial system. Analytical and numerical results show that, under certain conditions, an exact match is possible and the resulting fractional differential models have both a complex and frequency-dependent order of the differential operator. The implications of this type of approach for both model order reduction and model synthesis are discussed.