This paper is focused on a comparison between Model-Free Control and Active Disturbance Rejection Control methods. These two techniques do not require a detailed mathematical description of the system, since they base on the on-line estimation and rejection of the unmodeled elements of the dynamics. Robustness of the closed-loop control system (against external perturbation) and its relation to energy consumption is discussed here. A model of a planar manipulator is used in the conducted case study as an exemplary plant. Conclusions are supported with results obtained with numerical simulations.

 The problem of controlling stochastic linear systems with quadratic criterion which includes sensitivity variables is investigated. It is proved that the optimal full statefeedback control law with risk aversion can be realized by the cascade of mathematical statistics of performance uncertainty and a linear feedback. A set of nonlinear matrix equations are obtained, which constitutes the necessary and sufficient conditions that must be satisfied for an optimal solution.

 In this paper a fast algorithm to determine all stabilizing proportional controllers for single-input single- output systems with multiplicative uncertainty is considered.

 This paper presents a force feedback control approach to enhance the realism and the fidelity of an electric power steering simulator. This simulator includes a real steering system with its electric assistance and a DC motor intended for the load rack force restitution. This motor allows to compensate for the lack of real tires and to simulate the tire/road contact as in a real driving situation. Besides, two different scenarios case study are introduced to prove the effectiveness of the various algorithms: virtual sensor based on sliding mode observer for the unknown input estimation and a sliding mode control for the force feedback.