Background: The article is a summary of the possibility of using Petri layers in adaptive neuro-fuzzy controllers. In the paper the controller and two types of Petri layer have been presented, competitive layer which resets certain signals and transition layer which causes omission of signals. Material and methods: For the study, the simulation model in Matlab Simulink has been created. The model takes into account time constants of the electric motor and load. Mechanical part includes viscous and Coulomb friction. The flexible element was modeled using the elasticity constant and damping coefficient. Analyzed fuzzy controller with Petri layers was used as speed controller in cascade control structure of DC motor. Results: The results of a simulation showing the advantages and disadvantages of proposed solutions have been presented. Both quality of reference signal tracking and energetic cost of system work has been calculated. Conclusions: Main conclusions are that transition Petri layer can significantly reduce growth of numerical cost of the algorithm despite the increase of fuzzy rules count. Also both competitive Petri layer and transition Petri layer by changing some inner signals can affect output value of the fuzzy system and thus the control quality indicators change. Most positive solutions have been pointed out.