Adaptive ILC Design for Nonlinear Discrete-time Systems With Randomly Varying Trail Lengths and Uncertain Control Directions

In this article, an adaptive iterative learning control (AILC) scheme has been proposed to enhance the accuracy of the dynamic path tracking of 6-degrees of freedom industrial robots. Based on the memorized data and current feedback from a three-dimensional visual measurement instrument, an adaptive algorithm is developed to update the time-varying control parameters of the AILC scheme iteratively. A new compensation signal is calculated to adjust the control inputs produced by the dynamic path tracking control module at each time interval. Through the adaptation algorithm, the identical initial conditions can be relaxed to some extent with the AILC scheme. Moreover, the stability analysis of the proposed AILC scheme is presented. Experimental results on FANUC M20iA, using C-Track 780 as a photogrammetry sensor, demonstrate the superior performance of the developed AILC scheme in terms of pose accuracy, disturbance rejection ability, and control performance.

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Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach

Dou,

Prempain

2024

Sensors

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In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. By integrating the iterative refinement capabilities of traditional Iterative Learning Control (ILC) with the optimization strengths of gradient descent, Green ILC achieves a balanced trade-off between tracking accuracy and energy consumption. This novel approach introduces a cost function that minimizes both tracking errors and control effort, enabling the system to adaptively optimize performance over iterations. Theoretical analysis and simulation results demonstrate that Green ILC not only achieves faster convergence but also provides significant energy savings compared with traditional ILC methods. Notably, Green ILC reduces energy consumption by prioritizing efficiency, making it particularly suitable for energy-intensive applications such as robotics, manufacturing, and process control. While a slight decrease in tracking accuracy is observed, this trade-off is acceptable for scenarios where energy efficiency is paramount. This work establishes Green ILC as a promising solution for modern industrial systems requiring robust and sustainable control strategies.

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Adaptive ILC Design for Nonlinear Discrete-time Systems With Randomly Varying Trail Lengths and Uncertain Control Directions

Cited by 3 publications

References 48 publications

Data-Driven Robust Iterative Learning Predictive Control for MIMO Nonaffine Nonlinear Systems With Actuator Constraints

Data-Driven Robust Iterative Learning Predictive Control for MIMO Nonaffine Nonlinear Systems With Actuator Constraints

Adaptive iterative learning control for enhancing the dynamic path tracking accuracy of 6-degrees of freedom industrial robots

Green ILC: A Novel Energy-Efficient Iterative Learning Control Approach

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