Practical design of a time-varying iterative learning control law using fuzzy logic

Abstract

Iterative Learning Control (ILC) is an intelligent control algorithm that can effectively handle a tracking error in any system that operates in a repetitive manner. In practice, it is hardly possible to implement a single gain learning control law to improve the tracking performance due to the existence of large transient growth. To prevent the growth, this paper proposes a time-varying learning control design using the unique concept of fuzzy logic control to track the desired trajectory as well as the desired control input signal. The proposed control design is developed on both serial and parallel ILC configurations. The two configurations are initially constructed and implemented on a robotic manipulator with the use of a single gain learning control law. To avoid bad transients, the gain adjustment mechanism based on fuzzy logic control is introduced to vary the learning gain in each time step for enhancing the robustness of the system. According to the simulation and experiment on a robotic manipulator, both ILC structures with the proposed mechanism achieve the desired learning performance without bad transients.

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

“…4, is defined by a system equation in discrete time, featuring a sampling rate of 0.055 seconds. Numerous explorations have been conducted regarding the regulation of this robotic system, as demonstrated by the scholarly contributions of P. Chotikunnan et al [59], [70], [73], [74]. These scholarly efforts present a model for the system equation, identified as (1), incorporating the coefficients explicated in Table I.…”

“…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].…”

“…This study explores the combination of dual integral learners to capitalize on their individual strengths, inspired by [54]- [59]. The dual combining concept, merging fast and slow learners, strikes a balance between tracking performance and system robustness, demonstrating effectiveness across various applications.…”

“…The continuous pursuit of achieving precise robotic movement with minimal error has been a central focus of research. Extensive investigations into the Seiko D-Tran RT3200 robotic manipulator [50], [53], [59], [69], [70] have paved the way for the development of ILC [40], [71], [72], and repetitive control systems (RC), aiming to minimize the RMSE within the system. This research specifically utilized the system equation of the Seiko D-Tran RT3200 robotic manipulator to formulate an effective control system.…”

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

“…4 shows different robots that are compatible with the robotic vision system. These robots include the mechanical arm robot [91], [92], scala robot, and cartesian robot [93]- [97]. Additionally, industrial motors can be integrated into the robotic vision system.…”