Human brain capabilities to control are undeniable, but embedding that capacity in an algorithm for the control of a dynamic system has proven limited by natural human bounds such as the reaction time, which restricts the number of industrial applications using a human in the loop. Thus, the authors of this paper propose a new procedure to scale linear systems in time, which makes human control of dynamic systems not only feasible but also comfortable. The scaling method comprises moving poles and zeros of a transfer function proportionally to a scaling factor. Thus, a person controls the scaled version of the system, while the computer acquires his/her reactions, then a neural network learns those reactions. This network controls both scaled and original systems. The new control strategy controls slow and fast systems, as well as stable and unstable systems, achieving high performance for all conditions. Appropriate time scaling, and practice, facilitate the control of any dynamic system.
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