Learning in the synthesis of data-driven variable-gain controllers

To deal with performance trade-offs in the control of motion systems, a method is developed for designing variable-gain feedback controllers. The idea is to select a piecewise affine controller structure and, subsequently, to find the nonlinear controller parameter values of this structure by data-driven performance optimization. Herein an ℌ₂ performance objective is minimized. As a result, variable-gain controllers are synthesized using techniques from the field of learning and optimization. The method is applied to a wafer stage simulation model.