The viscoelastic properties of the lung have important implications during respiratory mechanics in terms of lung movement or work of breathing, for example. However, this property has not been well characterized due to several reasons, such as the complex nature of the lung, difficulty accessing its tissues, and the lack of physical simulators that represent viscoelastic effects. This research proposes an electropneumatic system and a method to simulate the viscoelastic effect from temporary forces generated by the opposition of magnetic poles. The study was tested in a mechanical ventilation scenario with inspiratory pause, using a Hamilton-S1 mechanical ventilator (Hamilton Medical) and a simulator of the human respiratory system (SAMI-SII). The implemented system was able to simulate the stress relaxation response of a Standard Linear Solid model in the Maxwell form and showed the capacity to control elastic and viscous parameters independently. To the best of our knowledge, this is the first system incorporated into a physical lung simulator that represents the viscoelastic effect in a mechanical ventilation scenario.

In this paper, a methodology for the decomposition of dynamical systems is presented. The methodology uses two decomposition strategies seeking to reduce the multiple solutions and heuristics that input-output pairing methods usually yield. The first strategy quantifies the interaction between the inputs and outputs of the system. Therefore from a large set of inputs and outputs it will be selected only the input-output pairings with a strong dependence and the remaining are discarded. The second strategy allows to obtain subsystems with local relevant states and make sure that each of them contains the adequate input-output pairings. Two typical benchmarks are used to test the methodology application, including the physical construction of a thermal benchmark. For each benchmark, distributed state estimation was performed. Results show that the use of the proposed methodology can help in the decomposition of a real large scale dynamical system and to improve the state estimation in decentralized and distributed schemes.

In this paper, a comparison of input-output pairing-based decomposition methods for distributed state estimation of large scale systems is presented. Three methods, namely Relative Gain Array, Niederlinski Index and Partial Relative Gain, are implemented as an initial step to decompose the system into subsystems. Subsequently the different subsystems configurations are compared by evaluating the centralized and local prediction error and the convergence of distributed Kalman-filter-based state estimators for each case. Simulation results are presented using a heat plate as test bed, spatially discretized, resulting in a large-scale linear system.

This paper explains the existence and observability conditions for the design of an Unknown Inputs Observer (UIO) for a chemical process. First, a phenomenological interpretation for the existence of the observer is given from the transfer function of the linearized system. And secondly, the existence and observability conditions of the observer are verified algebraically evaluating the rank of a defined observability matrix. Both ways of verification are compared. Finally, an UIO is implemented and simulation results are presented.