Efficient minimax control design for prescribed parameter uncertainty

The purview of this paper is the design of a controller that includes knowledge of parametric uncertainties and their distributions. The parameter distributions are approximated by a finite set of points that are calculated by the unscented transformation. This set of points is used to design robust controllers that minimize the worst performance of the plant over the domain of uncertainty. The proposed technique is illustrated on two benchmark problems. The first relates to the design of prefilters for a spring-mass-dashpot system and the second is the feedback control of a hovering helicopter. Numerical simulations are used to illustrate the proximity of the resulting controller to the minimax controller.