## 1 Introduction
The application is compared with the conventional PID controller.The authors in [29] present a control solution for Gantry robots based on a model reference adaptive controller and a fuzzy neural network as a motion compensator. The reference model is developed by reducing the tracking errors for a single axis, and the fuzzy neural compensator assures error compensations between dual servo-motors. The method efficiency is demonstrated by simulation. The authors in [30] present a control structure of a multi-input multi-output temperature control system based on neural model reference control. The paper developed a multi-input multi-output version of a neural model reference controller. The obtained control system improves the transient response of the system and maintains a constant temperature. Simulation is made using MATLAB/Simulink. Some new and important developments in the search for analytical solitary wave solutions for PDE are presented in [31; 32; 33; 34]. The results of this manuscript may well complement the existing literature. The advantages of neural predictive control are as follows: great capacity in predictive models; appealing attributes of non-linear identification and control; suitability for non-mathematical and complex models; ability to manage an abundant number of data and input variables; and trustworthy prediction. In addition, the disadvantages are that the neural network needs to be trained; it takes a long time to process large neural networks; a lot of time is expended for online training; and quality predictions need large amounts of data. Analyzing the literature, it can be seen that there are numerous papers that present studies with applications of fuzzy logic, neural predictive controllers, and neural model reference controllers in motion control problems. However, each of these studies, papers, and examples presents only various particular cases. In the literature there are no comparative studies of the three chosen methods applied to a specific case, which would explicitly show a comparison of these methods. For this reason, the idea of a comparative study of several methods on a theoretical case often used in practice is necessary. The paper covers precisely this missing comparison of three methods of computational intelligence, in a particular case, on a process model often found in theory and practice--positioning of a body in a rotational motion. The paper compares these methods with each other but also compares them with classical linear adjustment solutions, such as error feedback control, digital control, and state feedback control. Some controversial and diverging hypotheses are considered. Conventional position control is carried out using as execution elements electric machines driven by cascade control systems with internal current control loops, over which overlap a speed control loop and an external position control loop. This is the natural means of control. The current and speed control loops must respond as quickly as possible. In addition, the control of the position must be done asymptotically and aperiodically. In this paper, the angular positioning of a heavy weight object in rotational movement is taken into consideration. The inertia of the actuator is not taken in consideration, because it has a very small time constant compared to the moment of inertia of the moving object. Fuzzy logic brings human reasoning into controller design, and neural networks bring learning and training. The paper presents, in Section 2, the information related to linear and digital position control, the mathematical model of the positioning process, the motion process model and its limitations, the linear control system by error feedback, the digital control system with limitation, and the state feedback control system, their design, transient characteristics, and performance criteria as a base for comparison.