Transfer Learning for High-Precision Trajectory Tracking Through L_1 Adaptive Feedback and Iterative Learning

by   Karime Pereida, et al.

Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined L_1 adaptive control and iterative learning control (ILC) framework to achieve high-precision trajectory tracking in the presence of unknown and changing disturbances. The L_1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses L_1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high-level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined L_1-ILC framework compared with approaches using ILC with an underlying proportional-derivative controller or proportional-integral-derivative controller. Results highlight that our L_1-ILC framework can achieve high-precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller.


page 5

page 11


Adaptive Model Predictive Control for High-Accuracy Trajectory Tracking in Changing Conditions

Robots and automated systems are being introduced to unknown and dynamic...

Deep Neural Networks for Improved, Impromptu Trajectory Tracking of Quadrotors

Trajectory tracking control for quadrotors is important for applications...

Adaptive Feedforward Control For Reset Feedback Control Systems – Application in Precision Motion Control

This paper presents a novel adaptive feedforward controller design for r...

Adaptive Dynamic Programming for Model-free Tracking of Trajectories with Time-varying Parameters

In order to autonomously learn to control unknown systems optimally w.r....

Metaheuristics Based Modeling and Simulation Analysis of New Integrated Mechanized Operation Solution and Position Servo System

Inadequate environmental protection is a problem, and to address it, the...

Robust tracking of an unknown trajectory with a multi-rotor UAV: A high-gain observer approach

We study a trajectory tracking problem for a multi-rotor in the presence...

Please sign up or login with your details

Forgot password? Click here to reset