データ駆動型H₂制御性能評価の最適化計算における収束領域

抄録

This paper proposes a data-driven controller design method for minimum variance control and analyzes convergence properties of the method. Data-driven controller design methods tune control parameters by minimizing a criterion which is derived from a single set of input and output data without a process model. However, optimization problems in these methods are generally non-convex. The analytical results show that a gradient descent algorithm can converge from the set of initial parameter values to the global minimum if the maximum-phase difference between the initial controllers and the minimum variance controller is smaller than π/2 radians.