A human-machine model is introduced that accounts for operators’ limited working memory capacity in dynamic environments. How can air traffic controllers and their task environment be modelled over time, accounting for controllers’ adaptive behaviour in view of limited working memory capacity? From the literature it is known that workload can function as an independent variable effecting operators’ working methods. This finding is implemented within the following macro-cognitive human-machine model of a tower controller and his/her task environment which is realised with Coloured Petri Nets. The formal controller-airport model is based on action regulation theory and maps the effects of the limited working memory capacity on controllers’ working methods. Results for two capacity conditions show the effects of a high workload in such a work environment. The approach shows that macro-cognitive models represent a valuable addition to micro-cognitive models for modelling complex human-machine systems focusing on the interaction of operators and their task environment.