Time-Varying Sign Gain with Expert System in Serial Iterative Learning Control Architecture

Abstract

This study investigates the integration of time-varying sign gain with an expert system within the framework of serial iterative learning control architecture. Iterative Learning Control (ILC) is recognized as a vital technique for enhancing the precision of robotic systems. However, challenges persist in mitigating learning transients, which can affect system performance. To address this issue, an expert system is developed to fine-tune the learning control matrix. Through extensive simulation tests, the proposed approach's performance is evaluated, with a focus on reducing Root Mean Square Error (RMSE) and stabilizing robotic arm motion. The results demonstrate a consistent decrease in RMSE across multiple iterations, indicating the effectiveness of the integrated approach. Moreover, stability analysis reveals promising asymptotic convergence values, affirming the system's stabilization capability. In conclusion, this study advocates for the adoption of expert system techniques, such as the integration of time-varying sign gain, to manage learning transients and enhance the stability and precision of robotic manipulator applications.