Complex systems are often subjected to uncertainties that make its model difficult, if not impossible to obtain.  A quantitative model, that is a mathematical model in which the values of the parameters are real numbers, may be inadequate to represent the behavior of systems which require an explicit representation of imprecision and uncertainty.

When the uncertainties are structured, i.e. the model structure is known and only the parameters undergo imprecise, they can be handled with interval models in which the values of the parameters are allowed to vary within numeric intervals.

Robust control uses such mathematical models to explicitly have uncertainty into account. To solve robust control problems like finding the robust stability or designing a robust controller involves hard symbolic and numeric computation. When interval models are used, it also involves interval computation.

This paper presents a methodology and a framework that combines symbolic and numeric computation with interval analysis to solve robust control problems. The main advantage of interval analysis is that it provides guaranteed solutions.  

The framework IRCAD (Interval Robust Control framework for Analysis and Design) is implemented in Matlab. The application calls Maple, to manage symbolic data, and  C++ for interval and numerical computations. The implemented functionalities include: robust analysis tools like stability test and stability margin computation; robust control design tools, including a design specifications selecting tool; and a set of visualization and post-design tools to analize the behavior of the designed controllers and to verify the fulfillment of the requested specifications.