Control design for high-performance sampled-data systems with continuous time performance specifications is investigated. Direct optimal sampled-data control design explicitly addresses both the digital controller implementation and the intersample behaviour. The model that is required for direct optimal sampled-data control should evolve in continuous time. Accurate models for control design, however, generally evolve in discrete time since they are obtained by means of system identification techniques. The purpose of this paper is the development of a control design framework that enables the usage of models delivered by system identification techniques, while explicitly addressing both the digital controller implementation and the intersample behaviour aspects. Thereto, the incompatibility of the models delivered by system identification techniques and the models used in sampled-data control is analysed. To use models delivered by system identification techniques in conjunction with optimal sampled-data control, tools are employed that stem from multirate system theory. For the actual control design, key theoretical issues in sampled-data control, which include the linear periodically time-varying nature of sampled-data systems, are addressed. The control design approach is applied to the H-infinity-optimal feedback control design of an industrial high-performance wafer scanner. Experimental results illustrate the necessity of addressing the intersample behaviour in high-performance control design.