Advanced bioactuator selection for efficient human motion simulation in biohybrid robots using CSF dynamic knowledge

The selection of the most efficient actuator for biohybrid robots necessitates the implementation of precise and reliable decision-making (DM) methods. Dynamic aggregation operators (AOs) provide flexibility and consistency in DM by embracing time-dependent changes in data. The complex spherical fuzzy sets (CSFSs) adequately resolve multifaceted issue formulations characterized by spherical uncertainty and periodicity. This paper introduces two innovative AOs, namely, the complex spherical fuzzy dynamic Yager weighted averaging (CSFDYWA) operator and the complex spherical fuzzy dynamic Yager weighted geometric (CSFDYWG) operator. Notable characteristics of these operators are defined, and an enhanced score function is devised to rectify the deficiencies identified in the current score function in the CSF framework. In addition, the proposed operators are implemented to develop a methodical strategy for the multiple criteria decision-making (MCDM) situations to address the difficulties posed by inconsistent data during the selection procedure. These methodologies are also adeptly employed to address the MCDM problem, aiming to identify the most suitable actuator designed for precisely modelling human movement for biohybrid robots in CSF environment. Moreover, a comparative study is conducted to highlight the efficacy and legitimacy of the proposed methodologies in relation to the existing procedures.