Run-to-run control has been applied to several traditional batch processes in the chemical industry. The 24-h cycle of eating meals, measuring blood glucose concentrations, and delivering the correct insulin bolus, with the goal of achieving the optimal blood glucose profile, can be viewed in the same spirit as traditional batch processes such as emulsion polymerization. In this paper, we aim to exploit the "repetitive" nature of the insulin therapy of people with Type 1 diabetes. A run-to-run algorithm is used on a virtual diabetic patient model to control blood glucose concentrations. The insulin input is parameterized into the timing and amount of the dose while the glucose output is parameterized into the maximum and minimum glucose concentrations. Robustness of the algorithm to variations in the meal amount, meal timing, and insulin sensitivity parameter is addressed. In general, the algorithm is able to converge when the meal timing is varied within +/- 40 min. If the meal size is underestimated by approximately 10 grams (g), the algorithm is able to converge within a reasonable time frame for breakfast, lunch, and dinner. If the meal size is overestimated by 20-25 g, the algorithm is able to converge. When random variations in the meal timing and the meal amount are introduced, the variation on the output variables, Gmax and Gmin, scales according to the amount of variation allowed. Along with this, the insulin sensitivity of the virtual patient model is varied. The algorithm is robust for differences in insulin sensitivity less than +/- 50% of the nominal value.