Control of extrusion-based printing is fundamental to improving the traditional open-loop operation of commercial machines. However, the literature on feedback control based on nozzle motion is still limited in comparison with the developments for extrusion dynamics. In this work, we propose a model-based control strategy where the effects of the nozzle speed on the filament width are described by a first-order model, and the model uncertainty is used for robust synthesis. The feedback employs the internal model controller (IMC) to get an approximate inversion of the dynamics and the IMC filter is tuned based on robust performance criteria. The controller was experimentally tested and provided satisfactory results, especially when the parameters were refined depending on the speed regime.

In the heat treatment processes, offline utilization of first-principles models is well-established. These models tend to be complex, computationally demanding, and rely heavily on empirical relations. The fidelity of these models degrades over time due to changes in the process resulting in plant-model mismatch, which is typically attributed to an incorrect constitutive relation of a physical mechanism in the model (i.e. fouling in the heat exchangers). In this paper, we propose two hybrid modeling approaches, namely Sparse Identification of Nonlinear Dynamics with Control and least square estimation, to learn the dynamics of the discrepancy between the measurement data and the simulation model. The hybrid modeling approach is implemented on a heat exchanger network (HEN) example and it is shown that the accuracy of the first principles dynamic model is improved.