Robust Control With Fuzzy Based Neural Network For Robot Manipulators

  • 1 Faculty of Engineering, Department of Mechanical Engineering, Istanbul University - Cerrahpaşa, Türkiye


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.



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