## Effect of Weight and Diameter Variables on Balance Process for Inertia Wheel Pendulum by Using Swing Up and PID Controller

Machines. Technologies. Materials., Vol. 14 (2020), Issue 5, pg(s) 191-193

Inertia wheel pendulum balance control is performed by using swing up and PID controller with different wheel weight and diameters. In the pendulum control, 3 different radius wheels and different weights are added to analyze whether the system remained balance position. In this process, the effect of weight and diameter variables on the swing time and PID coefficients of the pendulum was observed. With this observation, the effects of input variables in the real-time system were compared with calculations in the dynamic pendulum model.

## Balance prediction of the inertia wheel pendulum by using swing up and PID controller

Trans Motauto World, Vol. 5 (2020), Issue 2, pg(s) 41-44

In this paper, inertia wheel pendulum balance control is performed by using swing up and PID controller. Paper provides predictions on real time design balance system. Predictions were performed through data that were classified and tested by machine learning via MATLAB. Data obtained a result of the analyze of balance positions and swinging times of the wheel different diameters and weights in real-time. Through to this work will be able to predictable which wheel characteristics can be controlled and balanced

• ## Implementation of INS for control of robots with a DC motor

Industry 4.0, Vol. 4 (2019), Issue 3, pg(s) 116-119

This paper describes the activity system and the importance of INS with the possibility of implementation to the robot control. The contribution also introduces the execution of DC motor regulation utilized for the positioning of a rotary positioned arm. The motor control comprises the current regulation, angular velocity and the rotation of the motor shaft fixed to the arm regarding the required angular change course of the arm rotation. The regulation structure of the DC motor is carried out in MATLAB/Simulink program. The arm movement is investigated via the mathematical model and virtual dynamic model formed in MSC.ADAMS program.

## IMPROVING THE PERFORMANCE OF AN INADEQUATELY TUNED PID CONTROLLER BY INTRODUCING A POLYNOMIAL MODEL BASED INCREMENT IN PID CONTROL VALUE

Mathematical Modeling, Vol. 2 (2018), Issue 1, pg(s) 8-12

In control literature, one can easily find a variety of different examples for industrial control, where contemporary control algorithms are implemented. Surprisingly, there are not many known examples where the state-of-the-art control algorithms have been implemented in real-time control systems. Instead, researchers usually implement algorithms that are proven to be reliable, fast and easy to implement. One control solution that has been proven to satisfy all previously mentioned attributes is PID. However, despite all its good assets it has two major deficiencies. One of them is that it can’t adapt on the diversities caused by the variation occurring in the model parameters and still it can’t control nonlinear systems due to multiple operating points present there. Therefore, to deal with those weaknesses an improvement in PID control structure has been introduced in the form of supervisory mechanism (SM) which as a main constitute part has a quadratic polynomial model. Thus, the control value of the newly proposed PID algorithm is formed of two terms, the first one is the value calculated by standard PID and the second one is the value calculated by the SM. The quadratic model forming part of the SM is obtained based on the past value of the error. Nevertheless, the use of quadratic model introduces additional complexity into the PID controller. Furthermore, the quadratic model should be updated fast enough and also it has to describe the data adequately. These aspects are analyzed and discussed in details in this paper. Moreover, an algorithm is introduced which will guarantee that the data used for calculation of the quadratic model is suitable.

## IMPROVING THE PRECISION OF PLANT RESPONSE BY MODELING THE STEADY STATE ERROR

Industry 4.0, Vol. 2 (2017), Issue 4, pg(s) 161-164