## 1 Introduction  
Electro-mechanical systems (EMs) are used widely in various industrial and home applications. The main advantage of the system is its high accuracy, high reliability, robustness, and low power to weight ratio. The EMs are unmissable components of the robotic, machine tool and feeder systems. The efficiency of the EMs is key for system performance and safety. The control design strategies for such kinds of systems mostly use a 2 DOF control structure with inner and outer feedback loops [1; 2; 3]. The aforementioned special 2 DOF approach is well-known, like the disturbance-observer algorithm (DBA), and can be found in different structures within several control paradigms [4; 5]. Most of the approaches use a nominal model in the inner feedback loop. The more convenient and straightforward method of DBA is a Q-filter design, and the more advanced is based on a robust internal compensator (RIC) [2; 5]. Bothe techniques deal with a trade-off between feedback performance and the robustness property [1]. However, the RIC approach is more transparent regarding the Q-filter method. A significant difference between the RIC and Q-filter is the design of their internal loops. The Q-filter approach uses the inverse nominal model of the system, whereby the RIC approach introduces a freely selectable structure. The freely selectable structure offers a broader possibility to tune the controller parameters with respect to the robustness property and the disturbance rejection capability of the internal loop [2]. It is important to notice that the internal loop is presented with matching disturbance as a load torque, and the controllers are mostly limited to the linear structures [1, 2, 4]. With regard to the listed facts, such as an imperfection in the system, nonlinear dynamics, parameters' uncertainty, output disturbance and static nonlinearities, the linear controllers can have a poor performance and restrict the reliability of the closed loop. The nonlinear control approach, compared to the linear, offers wider options and is more suitable in the design, where complex and strict requirements need to be fulfilled. Also, the nonlinear controller approach demands a more complex and sophisticated analysis, as well as profound knowledge of the controlled object. The latter is, in many designs, the biggest obstacle, which prevents use of the nonlinear controller. Among many nonlinear control paradigms, the sliding mode control (SMC) is one of the most reliable nonlinear techniques, where synthesized controllers have a simple structure and are straightforward to employ on real-time systems [6, 7]. The sliding mode concept is often used, due to its finite-time convergence and robustness to external disturbances and plant uncertainties. Despite the many useful properties of the SMC, the SMC strategies often suffer from unwanted chattering phenomena [8, 9]. Chattering phenomena can restrict the deployment of the algorithm on real-time systems due to the high wear of the system components and high heat losses. To avoid or suppress the unwanted chattering phenomena, the high-order sliding mode (HOSM) technique can be used [10, 11]. However, the use of the HOSM cannot completely guarantee that the chattering phenomena have vanished completely. Chattering phenomena can also be caused because of the unmodeled or parasitic system dynamics, a deeper discussion of which is given in [8, 9]. The discrete implementation of the controller is the subject of the chattering. In the discrete implementation, the amplitude of the chattering is proportional to the sampling interval. In recent years, a lot of researches have been focused on alleviating, or completely rejecting, the mentioned unwanted phenomena. The most often preferred technique uses an approximate signum function with an introduced transition boundary [10]. Similar to the signum function approximation is a boundary layer SMC approach, where the controller gain is adapted with regards to the vicinity of the sliding variable origin [12].