Improving Disturbance Estimation and Suppression via Iterative Learning Control among Dynamically Different Systems

Core Concepts

Combining iterative learning control (ILC) and disturbance observer (DOB) can enhance the robustness of dynamically different systems performing repetitive tasks by improving disturbance estimation and suppression.

Abstract

This study focuses on improving the robustness of unmanned aerial vehicle (UAV) trajectory tracking against external disturbances by combining iterative learning control (ILC) and disturbance observer (DOB) techniques. The key highlights are: The proposed methodology explicitly incorporates DOB as part of the ILC update process and considers differences in system dynamics among different UAVs to enable learning. The theoretical framework is developed for designing learning filters in conjunction with DOB to ensure the tracking error with learning is smaller than the error without learning, even in the presence of modeling uncertainties. Extensive simulations and experiments on three dynamically different quadrotor UAVs validate the effectiveness of the learning framework. The results show that the trajectory tracking errors and disturbance estimates are significantly improved with the proposed approach compared to using only DOB or no learning. The learning framework is tested under various reference trajectories and disturbance profiles, including stationary, circular, and diamond-shaped trajectories, as well as sinusoidal, impulse, and rectified sinusoidal disturbances. The framework demonstrates robust performance across these diverse scenarios. The learning is performed in a cyclic manner, where each UAV learns from the previous one, leading to convergence of the tracking error and disturbance estimation within a few iterations.

Stats

The root mean square error (RMSE) of the trajectory tracking is reduced from 40.65 cm to 2.34 cm for UAV#(1), from 30.77 cm to 1.88 cm for UAV#(2), and from 22.76 cm to 2.66 cm for UAV#(3) in the experiments.

Quotes

"By combining ILC and DOB, we can utilize the proactivity of ILC and the ability of DOB to adapt to new disturbances." "The designed methodology has undergone rigorous verification and validation through simulations and experiments." "The learning framework is tested under various reference trajectories and disturbance profiles, including stationary, circular, and diamond-shaped trajectories, as well as sinusoidal, impulse, and rectified sinusoidal disturbances."

Key Insights Distilled From

Improving Disturbance Estimation and Suppression via Learning among Systems with Mismatched Dynamics

by Harsh Modi,Z... at arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10231.pdf

Deeper Inquiries

How can the learning framework be extended to handle highly aggressive reference trajectories or time-varying disturbances

To extend the learning framework to handle highly aggressive reference trajectories or time-varying disturbances, several adjustments can be made. Firstly, the learning filters can be designed to adapt to varying disturbance frequencies and magnitudes by incorporating adaptive or robust control techniques. This would allow the system to dynamically adjust its learning process based on the characteristics of the disturbances encountered. Additionally, the framework can be enhanced by introducing predictive control strategies that anticipate future disturbances based on historical data, enabling the system to proactively respond to aggressive trajectories or rapidly changing disturbances. Furthermore, incorporating online optimization algorithms can optimize the learning process in real-time, ensuring optimal performance under challenging conditions.

Can the learning process be further improved by explicitly incorporating data from all previous systems, rather than relying solely on the previous system's learning signal

Improving the learning process by explicitly incorporating data from all previous systems can enhance the framework's performance and adaptability. By aggregating data from multiple systems, the learning algorithm can benefit from a more comprehensive dataset, leading to improved disturbance estimation and trajectory tracking. This approach can enable the system to learn from a wider range of experiences and adapt more effectively to diverse operating conditions. Additionally, utilizing advanced machine learning techniques such as transfer learning or meta-learning can facilitate the extraction of valuable insights from the combined dataset, further enhancing the system's learning capabilities.

What other applications beyond UAVs could benefit from the proposed iterative learning control with disturbance observation approach

The proposed iterative learning control with disturbance observation approach has broad applicability beyond UAVs and can benefit various systems in different domains. Some potential applications include industrial automation, robotic manipulators, autonomous vehicles, medical devices, and manufacturing processes. In industrial automation, the framework can enhance the performance of robotic systems in repetitive tasks, improving accuracy and efficiency. For robotic manipulators, the approach can optimize trajectory tracking and disturbance rejection, leading to more precise and reliable operations. In autonomous vehicles, the framework can enhance control systems to navigate complex environments and adapt to changing conditions effectively. Moreover, in medical devices and manufacturing processes, the approach can improve the robustness and accuracy of control systems, ensuring consistent and reliable performance.