A new approach for approximating the free transfer function in Q-parametrization

This paper presents a new approach for approximating the free transfer function in Q-parametrization. Its main advantage is that the number of parameters to be tuned during the design process is reduced, when compared to others methods. As a design example, the "two-mass and spring" benchmark control problem was solved, using GESA: an evolutionary non-linear optimization technique