ABSTRACTBioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters, such as pressure, nozzle size, and speed, have a large influence on the quality of the bioprinted construct. Moreover, cell suspension density, cell culture period, and other critical biological parameters directly impact the biological function of the final product. Therefore, an approximation model that can be used to find the values of bioprinting parameters that will result in optimal bioprinted constructs is highly desired. Here, we propose type-1 and type-2 fuzzy systems to handle the uncertainty and imprecision in optimizing the input values. Specifically, we focus on the biological parameters, such as culture period, that can be used to maximize the output value (mineralization volume). To achieve a more accurate approximation, we have compared a type-2 fuzzy system with a type-1 fuzzy system using two levels of uncertainty. We hypothesized that type-2 fuzzy systems may be preferred in biological systems, due to the inherent vagueness and imprecision of the input data. Here, our results demonstrate that the type-2 fuzzy system with a high uncertainty boundary (30%) is superior to type-1 and type-2 with low uncertainty boundary fuzzy systems in the overall output approximation error for bone bioprinting inputs.
Adaptive Backstepping Fuzzy Control Based on Type-2 Fuzzy System
