Fidelity of Cyber-Physical Empirical Methods

Abstract

Cyber-physical empirical methods enable to address problems that classical empirical methods alone, or models alone, cannot address in a satisfactory way. In CPEMs, the substructures are interconnected through a control system that includes sensors and actuators, having their own dynamics. The present paper addresses how the fidelity of CPEMs, that is the degree to which they reproduce the behaviour of the real system under study, is affected by the presence of this control system. We describe an analysis method that enables the designer of a CPEM to (1) identify the artefacts (such as biases, noise, or delays) that play a significant role for the fidelity, (2) define bounds for the describing parameter of these artefacts ensuring high-fidelity of the CPEM, and (3) evaluate whether probabilistic robust fidelity is achieved. The proposed method is illustrated by considering a substructured slender structure subjected to dynamic loading.

Optimal Actuator Placement for Real-Time Hybrid Model Testing Using Cable-Driven Parallel Robots

“…For similar reasons, we scale moments with gain of 0.5 relative to translational forces (applies to κ 1 ). We leave κ (•) [1] and κ (•) [2] as priority parameters κ x and κ y . 4Considering that the cost function value is typically low (due to division by α 2 ), we set κ g = 10, which ensures that the term has a moderately smaller impact on the global performance measure function than the other terms.…”

“…Real-time hybrid model testing (ReaTHM testing) is a cyber-physical empirical method for emulating complex ocean structures that combines numerical models with traditional hydrodynamic model testing [1,2]. This is done by partitioning the ocean structure under consideration into a numerical substructure and a physical substructure that are coupled in real time through a measurement and control interface.…”

“…Next, the entries of κ 1 , κ 2 , and κ 3 are determined in proportion to the expected dynamic range and the variations of η and w re f . For example, an expectation of large variations in η [1], corresponds to an increase in κ 2 [1] and κ 3 [1], as these scaling parameters capture sensitivity to changes in η [1]. Conversely, an expectation of large variations of w ref [1] corresponds to an increase in κ 1 [1], since this scaling parameter captures sensitivity to w ref [1].…”