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

The integration of robotic systems is widespread across diverse industries, notably in defence, automotive, and industrial sectors. These systems are endowed with the capability to execute precise movements via software programming, facilitating object manipulation and trajectory adjustments. Nonetheless, careful oversight is imperative during operations to avert undesirable outcomes stemming from mishandling. Consequently, the management of robotic systems has emerged as a pivotal concern in contemporary industrial practices. The parameters governing robotic systems are subject to fluctuations contingent upon the loads they bear. Robust control, a methodology geared towards adapting the control system to accommodate such parameter variations, stands as a cornerstone for ensuring stability and optimal performance. This approach enables the maintenance of desired control levels even amidst shifting system parameters. To refine controller parameters, an objective function derived from error functions of the first and second robot arms was minimized. In this endeavour, the particle swarm optimization (PSO) method, renowned for its efficacy, was employed. The efficacy of this proposed control methodology is substantiated through graphical representations, underscoring its utility and effectiveness in real-world applications.

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.

The paper deals with innovations in vehicle suspension technology developed in the Josef Bozek´s Research Center of Combustion Engines and Automobiles at CTU in Prague, Czech Republic. A unique innovative suspension system that uses a linear electric motor as a controlled actuator has been designed. Many experiments on the energy management in the system have been accomplished. In order to verify various control strategies and to test different ways of energy consumption optimization we designed and constructed a unique onequarter- car test stand. To realize simulation and practical experiments at the test stand it is necessary to find a proper experimental road disturbance signal to excite the active suspension system. The disturbance signal is applied on one more linear motor that is placed under a wheel of the one-quarter-car test stand to excite the active suspension system. The paper deals with the way and results of experimental verification of vehicle active suspension behavior when robust control is applied and also with energy management strategy that is used in the system. A modified H-infinity controller that enables to set energy management strategy is mentioned in the paper. At the close of the paper, some experiments taken on the one quarter-car model and their evaluation are discussed.

The paper deals with the design and experimental verification of a new control structure with reference model for the tension control in the technological processing line the stability of which is derived on basis of the second Lyapunov method. The properties of the proposed controller were verified by experimental measurement on a new concept of hardware-in-the-loop (HIL) simulation platform based on programmable logic controllers (PLC). The experimental measurements confirmed that the proposed control structure is robust over a wide range of controlled system significant parameters changes together with invariance and desired dynamics prescribed by the reference model.

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.

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.