Current advanced methods for glucose control in people with type 1 diabetes (T1D), often referred to as artificial pancreas (AP) or automated insulin delivery (AID) systems, rely on the administration of a single hormone (insulin) to regulate blood glucose (BG). In general, these systems depend on patient-specific information usually obtained from the conventional insulin therapy to account for inter-patient variability. On the other hand, dual-hormone (DH) systems that use insulin and its counterregulatory hormone, glucagon, have the potential of further improving BG control. However, DH systems are still under development or in earlier testing stages. Since glucagon is not used in the traditional therapy for T1D, the sensitivity of each individual to this hormone is typically unknown. Here, a DH controller based on robust control is proposed. The controller in charge of glucagon dosing, based on H∞ optimal control, does not require any individualization, thus overcoming one of the challenges faced by DH approaches. The strategy is evaluated in silico and compared to previous works involving a personalized glucagon controller and its single-hormone counterpart. Results suggest that the robust control strategy allows satisfactory glucose outcomes without the need for individualization.