Evaluating a Grey-Box System Identification Module for a Digital Twin

A key element of any digital twin is the digital replica, or model, of its physical counterpart. When applied to industrial automation systems, it is important to consider the trade-off between model fidelity and computational complexity when developing this model. In this paper, we investigate the use of grey-box modelling as a means of reducing computational complexity while maintaining model fidelity. Two sets of tests are performed on a three-link robotic manipulator to evaluate the effect of input disturbances on the fidelity of the digital twin’s system identification module. The results of the test show that the system identification model’s parameter estimation is sensitive to input disturbances; however, despite this sensitivity, the model is able to accurately predict the dynamic response of the robotic manipulator.