Norm Optimal Iterative Learning Control for Non-Repetitive Trajectory Tracking of Servo System

Jonnalagadda, Vimala Kumari; Elumalai, Vinodh Kumar

doi:10.15866/ireaco.v14i3.20692

Cited by 1 publication

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“…Q-filters, designed using low-pass filters [37], eliminate highfrequency disturbances [38]- [41]. Optimization-based designs, including norm-optimal ILC [43], offer a trade-off between tracking error and input updates [43]- [48]. Robust learning control matrices, designed for system robustness, may neglect high-frequency components, leading to non-zero tracking errors [51]- [53].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Parallel Iterative Learning Control with A Time-Varying Sign Gain Approach Empowered by Expert System

Chotikunnan,

Minyong

2024

Journal of Robotics and Control

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This study explores the incorporation of time-varying sign gain into a parallel iterative learning control (ILC) architecture, augmented by an expert system, to enhance the performance and stability of a robotic arm system. The methodology involves iteratively tuning the learning control gains using time-varying sign gain guided by an expert system. Stability analysis, encompassing asymptotic and monotonic convergence, demonstrates promising results across multiple joints, affirming the effectiveness of the proposed control architecture. In comparison with traditional PID control, fixed gain ILC, and ILC with adaptive learning in the expert system, the analysis focuses on stability, precision, and adaptability, using root mean square error (RMSE) as a key metric. The results show that ILC with adaptive learning from the expert system consistently reduces RMSE, even in the presence of learning transients. This adaptability effectively controls the learning transients, ensuring improved performance in subsequent iterations. In conclusion, the integration of time-varying sign gain with expert system assistance in a parallel ILC architecture holds promise for advancing adaptive control in robotic systems. Positive outcomes in stability, precision, and adaptability suggest practical applications in real-world scenarios. This research provides valuable insights into the implementation of dynamic learning mechanisms for enhanced robotic system performance, laying the groundwork for future refinement in robotic manipulator control systems.

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