This thesis presents a study on the subject of robust and non parametrical control, more precisely related to the Mixed H2/Hinf problem. The main purpose of this work consists in the controllers design by a direct approach over the mentioned problem, using Galerkin`s method. Two numerical methods were proposed, being known as Pre- Established Basis Expansion (EBPE) and Optimized Basis Expansion (EBO). For the application of these methods, the stabilizing controllers of the system under analisys must be explicated by Youla`s parametrization. After this, a new parametrization is performed in order to set the problem in a standard format, with the Hinf-norm in the Nehari problem. A controller computed by EBO or EBPE has a previously specified order, internally stabilizes the plant, minimizes a performance functional and satisfies a pre- established robustness stability margin. In the EBPE method, a basis is chosen from space solution and the adjustment of the vectors` coefficients is performed minimizing the proposed criterion. The advantage is solve a convex optimization problem. By the other side, the choice of vectors in the truncated basis will be defined, leading to a solution with a higher order than the one obtained by the EBO approach, for the same performance level In the EBO method, both the vectors` choice in the truncated basis and their respective coefficient adjustment are performed simultaneously. The Youla`s parameter is approximated by the class of real, rational, proper and stable transfer function and the project variables are the coefficients of the numerator and denominator polynomials. Although this problem is a non- convex one, its main advantage lies in the possibility of finding controllers of relatively lower orders than ones by EBPE at the same level of performance. In addition, some theoretical results are discussed, where the existence and uniqueness of Mixid H2/Hinf problem solution are proved. It is also shown that the sequence of controllers computed by the proposed methods, as order increase, converges to the solution of the original problem.