International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This article describes an experimental setup for the practical teaching of process control in an undergraduate mechanical engineering curriculum. The system is practice-oriented and is based on developing students’ skills in control system application of real process systems. A PLC (Programming Logic Controllers) program is proposed to introduce simulation aspects and final control design practices in scale processes (Unloading and Handling Station). It is based on the principle of introducing a PLC-supported, Siemens Simatic S7-200 PLC controller-controlled production system with the help of SIMATIC Step 7 Programming Software, presenting the scenario for the system and writing the PLC program accordingly. This algorithm prepared for the Unloading and Handling Station operation is not unique and without alternatives. The aim here is to enable students to get to know the system and program the system with different approaches according to the algorithm they have developed.

Bipedal, four-legged and humanoid robots are rapidly increasing in our lives. The design and development of robots continue in similar. For this reason, the importance of studies on bipedal robots is also increasing. In this study, the design of a biped robot, forward and inverse kinematics computation were made. Bipedal robot walking pattern and joint angle changes were determined according to the kinematic model of the robot and the power of the motors to rotate the joints. As a result of the walking pattern, center of mass and zero moment point change were tracked. As a result of the study, the stepping style, walking motion, gait analysis, changes in joint angles were determined of a bipedal robot.

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.

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