## 1 Introduction  
Since a kilowatt-hour of electrical energy transformed into mechanical energy can produce an impressive amount of human work, current and future well-being depend primarily on automatic systems concerning many key areas for humanity, including the transportation and manufacturing systems. Hence, it is of great relevance to develop control algorithms that can be easily implemented using modern digital and wireless technologies to force electromechanical systems to behave like skilled workers who work quickly, accurately, and cheaply despite parametric variations, nonlinearities, and persistent disturbances. In particular, special attention has been paid to solving robust tracking control problems, which resulted in a considerable number of publications (see, e.g., [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43] and references therein). However, there exist many results obtained under the following simplified hypotheses, which are not always realistic and feasible: (1) exact knowledge of the controlled system is assumed; (2) actuators are considered ideal; and (3) signals are assumed measurable without noises, therefore, ideal or almost ideal derivative actions can be used. The robust tracking control mostly uses the well-known feedback linearization, inverse model and model predictive control (MPC) techniques. Feedback linearization is the process of determining a feedback law and a change of coordinates that transform a nonlinear system into a linear and controllable one. Inverse modeling is a general mathematical method to determine unknown causes on the basis of observation of their effects, as opposed to modeling of direct problems whose solution involves finding effects on the basis of a description of their causes. MPC is a very popular controller design method in the process industry for discrete-time models. Taking also into account that the computation time for feedback linearization or control signal generation by the inverse model or MPC techniques is non-negligible, these simplified hypotheses make the above mentioned techniques not always reliable (see [4; 5; 6; 8; 10; 14; 15; 20; 23; 24; 25; 29; 31; 33; 37; 40; 41]). In some cases, the control system can even be unstable, as can be verified with simple examples (see also Appendix A). Furthermore, the control techniques using high-frequency and high-amplitude control signals do not yield good performance (see also Appendix A). To avoid the aforementioned problems, various modifications of the inverse model and feedback linearization techniques have been proposed, for instance, a computed-torque-like control with variable-structure compensation (see [20; 25; 26]), which is, however, difficult to design and implement and also presents high-frequency oscillations (see also Appendix A). Thus, there is still a demand for smooth robust controllers that allow a plant belonging to a broad class of nonlinear uncertain systems, including electromechanical ones, to track a sufficiently smooth reference signal with a tracking error norm smaller than a prescribed value, despite the presence of disturbances and parametric and structural uncertainties, using real actuators and real derivative actions. In some previous author's contributions (see [21; 34; 35]) some control methodologies of MIMO uncertain linear systems and of a class of MIMO nonlinear uncertain systems have been provided in order to design simple controllers: state feedback controller with integral action, a pseudo-PD and pseudo-proportional-integral-derivative (pseudo-PID) to track sufficiently smooth trajectories with an a priori given maximum error.