Data-Driven Parameter Optimization of Two-Degree-of-Freedom Controllers Approximated by Laguerre Expansions*

In this paper, we propose one of the parameter optimization techniques for two-degree-of-freedom (2DOF) controllers based on the direct use of the data to achieve the desired tracking property. The proposed method utilizes only one-shot experimental data for the optimization of the feedforward controller approximated by Laguerre expansions with tunable coefficients. The proposed cost function to be minimized leads to the attainment of both of the desired controller and the mathematical model of the plant. Finally, we also illustrate a numerical example to show the validity of our proposed method.