SummaryResearching the theory and applications of general type‐2 fuzzy logic systems (GT2 FLSs) has become a hot orientation in recent years. The permanent‐magnetic drive (PMD) affected by uncertainties is an emerging technology. This paper designs a type of Takagi‐Sugeno‐Kang GT2 FLSs to investigate PMD temperature forecasting problems. Genetic algorithms are used to optimize the parameters of Takagi‐Sugeno‐Kang GT2 FLSs, according to the asymptotic way. The primary membership functions (MFs) of the antecedent and input measurements of the proposed systems are chosen as the Gaussian‐type MFs with uncertain standard deviations, and the consequent parameters are selected as crisp numbers, whereas the secondary MFs (vertical slices) are selected as a triangle type. Noisy data of PMD temperature are used for training and testing the proposed T2 FLS forecasting methods. Numerical simulation studies and convergence analysis illustrate that the proposed GT2 FLSs outperform their type‐1 and interval type‐2 FLSs.