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

The utilization of robotic systems is prevalent in various industries, such as defence and automotive, and is commonly utilized in industrial settings. The movements of these systems can be controlled through software programming, allowing for the manipulation of objects and modification of trajectory as desired. However, it is important to exercise caution during these operations as improper manipulation may result in undesired outcomes. As a result, the control of robotic systems has become a crucial aspect in modern industry.
The parameters of robotic systems are subject to change based on the loads they carry. Robust control is a method that adapts the control system to accommodate these changes in parameters, thereby maintaining stability and performance. This control method allows for the desired level of control to be maintained even in the presence of changing system parameters. In contrast to traditional robust control methods, robust control utilizes variable parameters with a constant upper limit for parameter uncertainty. Control parameters are updated over time using cosine and sine functions, however, determining appropriate values for these parameters can be challenging. To address this issue, a neural network model utilizing fuzzy logic compensator is employed to continuously calculate the appropriate control parameter values. The effectiveness of this proposed control method is demonstrated through graphical representation.

Parameters of the robots are always changed due to the load being carried. Robust control is a method that considers the changes of control system performance related to the modification of system parameters. Stability and performance of the system can be well protected in case the change of system parameters does not affect the system. Even if there is several modified parameters, robust control system still provides the ability of control in a desired manner.

In this work, parameters are made changeable and the upper limit of the uncertainty parameter is kept constant unlike other robust control studies. Control parameter is updated over time depending on the trigonometric functions. The values of the constant control parameters in trigonometric functions affect the performance of the system and it is quite difficult to find appropriate control parameter values. Logical fuzzy compensator is designed to find this parameter and investigated the effects on the tracking error of two-link robot.

Fuzzy Logic associated robust control methods developed using robust control has been compared through a computer simulation using the same trajectory and same model. Thanks to the designed fuzzy logic associated robust controller, robust control is improved and two-link robot’s trajectory tracking error has been reduced to a very small value.

In this paper, an adaptive control method is improved for airfoil model. By using energy method, the governing equations of the nonlinear 2-D airfoil model are obtained. As known, the system exhibits different behaviors at different speeds. For this purpose, flutter speeds are investigated and phase portraits of pitching are shown at critical speeds. Flutter for airfoils is such an enormous problem which have been considered. An adaptive control method is improved for minimizing the vibration at pre-flutter speed, flutter speed and post-flutter speed regimes. To show that the controller system works, controlled and uncontrolled airfoil model are simulated simultaneously. Results of these simulations are demonstrated graphically at the conclusion part.

Due to its compatibility and functionality, segways have been widely used in many countries. It was first introduced in December 2001.Yet, segway robots are faced with problems such as friction and external disturbances. Therefore, some controllers are designed to overcome with these problems. In previous studies, traditional controllers are used to balance a two-wheeled segway robot. The aim of this study is to minimize the trajectory tracking error. Due to external disturbances, such as wind, force and torque, robot parameters cannot be calculated exactly. Hence, the parameters of the robot are assumed to be unknown. In such situations, adaptive and robust controllers give better results. Adaptive and robust control laws were examined and adaptive-robust system was designed for the segway robot. Then Lyapunov function was defined and this adaptive-robust controller was derived from the Lyapunov function. And this control system applied to a two-wheeled segway robot model.