\right)\, dt +  \Vert x(t_f)-\xi(t_f)\Vert_Q^2 ,$$ with weighted norms (see [@Anderson; @Kwakernaak; @Trelat1]). The first term in the integral measures how close one is to the reference trajectory, the second one measures a $L^2$ norm of the control (energy), and the third one accounts for the distance at final time between the reference trajectory $\xi(\cdot)$ and $x(\cdot)$. Then, the control can be expressed as a feedback function of the error $x(t)-\xi(t)$, involving the solution of some Riccati equation. In [@AndreaNovel2013; @Khalil], the authors investigate the question of stabilizing around a slowly time-varying trajectory. They also introduce uncertainties on the model and study the sensitivity of the system to those uncertainties. In the case of the existence of a delay on the input, a feedback law is proposed. In [@Lin2007; @Tan2009], uncertainties $p$ are introduced in a linear system $\dot{x}(t) = A(p)x(t) + B u(t)$, and a tracking algorithm is suggested, under matching conditions on the uncertainties or not (see also [@Abdallah1991] for a survey on robust control for rigid robots).

In the late 1970’s, $\mathcal{H}_{\infty}$ control theory developed. The control system is often described by a plant $G$ and a controller $K$. Then, the dependency of the error $z$ (to 