## 1 Introduction  
This paper presents the results of a comparative study of motion control methods from computational intelligence, applied to positioning of an object, with known moment of inertia, in the rotational movement in the plane around a central axis. Angular positioning is used among many domains such as mechatronic systems, robotics, and automotive, and on various rotating mechanisms such as solar array drive mechanisms, antenna pointing mechanisms, scientific instruments, motors, and actuators. This paper includes examples of modeling, control, and optimization in order to increase control precision and to reduce the response time. The purpose of this work is the analysis of methods of computational intelligence for positioning based on state feedback fuzzy control, neural predictive control, and neural model reference control compared with the linear control methods such as error feedback control, numerical control, and state feedback control. The significance of this study is that it allows the comparison of the values of the empirical performance criteria obtained for the tested control systems in the case of the position control of an object described by a valid general mathematical model. In the literature there is an extremely large number of papers that addresses the issue of position control, using various control techniques. The following is a brief overview of recent works to highlight the current state of the art. In [1], a control method is proposed to regulate the aerial manipulation position in free flight of an under-actuated quadrotor UAV. The control is made in a bilateral teleoperation manner to regulate the aerial manipulator position and the applied force. The control system may be used in hazardous conditions. An application to control the pendubot based on partial linearization and stabilization in the upright position is presented in [2]. The paper investigates the closed loop system and its zero dynamics by simulation. The paper [3] evaluates various techniques of modeling, analysis, and control on a simple flexible robotic arm taken as a test model. The load mass position is considered as a time-dependent parameter. A complex model is considered for the flexible arm, and the linearization is made with finite element analysis. A robust controller designed based on H-infinity technique is used. The paper [4] presents a control solution to maintain an underwater vehicle at a desired static position under environmental disturbances. Non-linear dynamics is considered for the vehicle. The motion control is made based on the sliding mode concept. Two methods for the optimal allocation control module including the least squares method and the quadratic programming method are used. The results show stability and small steady-state error and robustness. Reference [5] includes a series of 14 research studies that cover different sub-areas in the framework of motion planning and control. Computational intelligence refers to the ability of a system to learn a specific task from data or experimental observation. It is a set of nature-inspired computational methodologies and approaches to address complex real-world problems to which mathematical or traditional modeling can be useless for a few reasons: the processes might be too complex for mathematical reasoning, or it might contain some uncertainties during the process. The methods used are close to the human's way of reasoning, i.e., it uses inexact and incomplete knowledge, and it is able to produce control actions in an adaptive way. Computational intelligence uses a combination of techniques: fuzzy logic, which allows the computer to understand natural language; and artificial neural networks, allowing the system to learn experimental data functioning such as the human mind. The authors in [6] conduct a review of some mechatronic applications based on smart motion control technology. Fuzzy logic is widely used in machine control.