## 1 Introduction
The third section presents the computational intelligence methods used for position control: state feedback fuzzy control, neural predictive control, and neural model reference control. The fuzzy variables, as state variables, were chosen as were their membership functions. A set of 27 fuzzy reasoning control rules was developed. The min-max inference method was used, and defuzzification was based on center of gravity. The structures based on neural network model reference control and neural model reference control are presented with their design. The chosen neural prediction controller was the basic model, in which the process model is implemented with the help of a feedforward neural network, and the optimization calculation is conventional. A neural model of the process was developed based on neural identification of the motion model, testing, and validation. The neural controller was also trained. The methods were modeled and simulated in MATLAB/Simulink. The results obtained with these three methods are presented in Section 4. The characteristics obtained with the linear control methods are presented in the Appendix. The characteristics obtained by simulations are discussed, compared, and analyzed in Section 5. The analyzed methods ensured good performance criteria: zero control error in steady-state, reduced response time and zero overshoot, and asymptotic and aperiodic behavior. The performance criteria differences between the control methods were small. The work's main contributions and novelty are:  
* Comparative analysis of computational intelligence control methods, namely fuzzy control, neural prediction control, and neural model reference control in terms of performance criteria of control, in the case of adjusting the position of a body in rotation movement around a central axis, in variable angular positions, and using as a comparative base three linear control structures, namely error feedback control, digital control, and state feedback control. * The chosen control structures were modeled and simulated in MATLAB/Simulink. * For each chosen control method, the structure of the control system, the sizing mode, the regulator parameters, the training parameters, the testing and validation of the neural networks, the characteristics of transient and stationary regime, and the values of the regulation performance criteria are presented. * This paper demonstrates that for all the analyzed control methods, good values of the regulation performance criteria can be obtained--low response time, zero overshoot, aperiodic and asymptotic variation of the position, and zero error in the steady-state. * Analyzing the transient regime characteristics resulting from the six studied control methods, it can be said that the differences between the efficiency criteria of the control structures based on computational intelligence method and linear methods are small. * This paper demonstrates that if the motion process has a linear model, the values of the performance criteria of the regulation have close values, and good quality of the regulation is ensured. However, if the motion process is nonlinear, with the help of fuzzy and neural control methods, better values of the control performance criteria can be obtained, ensuring robustness to the identification of the parameters and to the action of the disturbing load torque.