Abstract
Designing a digital twin will be crucial in developing automation-based future manufacturing systems. The design of digital twins involves data-driven modelling of individual manufacturing units and interactions between the various entities. The goals of future manufacturing units such as zero waste at the plant scale can be formulated as a model-based optimal control problem by identifying the necessary state, control inputs, and manipulated variables. The fundamental assumption of any model-based control scheme is the availability of a “reasonable model”, and hence, assessing the goodness of the model in terms of stability and sensitivity around the optimal parameter value becomes imperative. This work analyses the data-driven model of an acetaminophen production plant obtained from SINDy, a nonlinear system identification algorithm using sparse identification techniques. Initially, we linearize the system around optimal parameter values and use local stability analysis to assess the stability of the identified model. Further, we use what is known as a conditional sloppiness analysis to identify the sensitivity of the parameters around the optimal parameter values to non-infinitesimal perturbations. The conditional sloppiness analysis will reveal the geometry of the parameter space around the optimal parameter values. This analysis eventually gives valuable information on the robustness of the predictions to the changes in the parameter values. We also identify sensitive and insensitive parameter direction. Finally, we show using numerical simulations that the linearized SINDy model is not good enough for control system design. The pole-placement controller is not robust, and with high probability, the control system becomes unstable to very minimum parameter uncertainty in the gain matrix.