Due to the nature of the plant (the presence of uncertainties and nonlinear), a better option may be considered the use of modern systems. Such example is the fuzzy controller which was just simulated in [11]. In [12] a new version of fuzzy controller is proposed (simple-structured FLC with the skew-symmetric property in the control rule table) by keeping the same performances. In [13] evolutionary approaches for designing rotational inverted pendulum controller are presented, including genetic algorithms GA, particle swarm optimization PSO, and ant colony optimization ACO methods. The experiments were made using of xPC-Target toolbox and input-output PC card in Matlab/Simulink environment. The authors of [14] applied ANN algorithm to identify the system and then to control the rotary inverted pendulum using the Matlab environment and DSPs.