The aim of robust control is to guarantee closed-loop stability and a desired system performance in the presence of plant uncertainties, external disturbances and measurement noise. Model uncertainties may be parametric uncertainties or may be due to a simplified system description that neglects some physical effects. The chapter considers unstructured and structured model uncertainties categorised into additive and multiplicative ones. They are usually pulled out of an uncertain plant model. The result in a standard extended control configuration with a nominal plant model as part of a controller feedback loop and a feedback loop with a signal block collecting all uncertainties assumed to be stable and norm bounded. If knowledge about the structure of the perturbations is available, they are called structured; otherwise, unstructured if they are only assumed to be norm-bounded. Performance requirements can be achieved by choosing performance weights on the sensitivity and the complementary sensitivity function. Typical requirements such as reference tracking, noise attenuation or disturbance rejection can be met by finding a controller that minimises the \(\mathrm {H}_{\infty }\) -norm on the transfer function matrix relating exogenous (unwanted) inputs to output control errors. Performance specifications can be integrated in an \(\mathrm {H}_{\infty }\) -framework by adding a fictitious uncertainty block accounting for \(\mathrm {H}_{\infty }\) -performance specifications to the block of model uncertainties.