An Intelligent Optimized Gain Scheduling Proportional-Integral-Derivative Controller for Bearing Application

The Gain scheduling approach is utilized in many control systems and mechanical applications. In addition, the present research is focused on Magnetic bearing performance improvements such as rolling and Speed function. Usually, the function of the bearing is controlled by any kind of control system. However, the poor controller has provided the average bearing control performance. For this, the current article has aimed to design a novel Bat Recurrent-Gain Scheduling-Proportional-Integral-Derivative (BR-GSPID) controller to tune the bearing functions. Here, the function of the GSPID control parameters is regulated by the Bat Recurrent (BR) algorithm. Then the performance of the designed BR-GSPID control system is tested for the bearing application. Initially, the GSPID is modeled in the MATLAB platform for the linear parameter Varying (LPV) systems. Subsequently, the accuracy and error parameters of the BRGSPID are validated and compared with other recent and have earned the best high accuracy and less error measure.