Transfer learning for high‐precision trajectory tracking through adaptive feedback and iterative learning

Abstract

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  1 adaptive control and iterative learning control (ILC) framework to achieve high-precision trajectory tracking in the presence of unknown and changing disturbances. The  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  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  1 -ILC framework compared with approaches using ILC with an underlying proportional-derivative controller or proportional-integral-derivative controller.Results highlight that our  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. KEYWORDSiterative learning,  1 adaptive control, trajectory tracking, transfer learning INTRODUCTIONRobots and automated systems are being deployed in unstructured and continuously changing environments. Sophisticated control methods are required to guarantee high overall performance in these environments where model uncertainties, unknown disturbances, and changing dynamics are present. Examples of robotic applications in unknown 388

An Advanced Unmanned Aerial Vehicle (UAV) Approach via Learning-Based Control for Overhead Power Line Monitoring: A Comprehensive Review

“…Content may change prior to final publication. Another attempts to address the disturbance using the ILC in combination with different control approaches have been conducted in [144], [145]. In [146], the authors designed back-stepping integral sliding mode control (BISMC) with ILC algorithm for quadrotor.…”

“…Finally, iterative learning control is designed to improve the accuracy of the tracking. Other approach, the authors in [145] introduced a design based on the capabilities of L adaptive control combined with ILC form to achieve high-precision trajectory tracking in the presence of unknown and changing disturbances.…”

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“…To bridge this gap, recent methods such as [14], [15] include both a complex model trained on lots of data with a simple online adaptation term to that can be updated quickly to adapt to new, previously unseen tasks. The simplicity of this online learning term enables fast adaptation to new conditions without worrying about overfitting or gathering sufficient data to do a complex model identification and validation.…”

Editorial for the Special Issue on Learning‐based Adaptive Control: Theory and Applications

“…A hybrid integrator‐proportional controller is used together with data‐driven optimal gains autotuning to improved low‐frequency disturbance rejection properties without deteriorating overshoot and settling‐time performances. The challenging problem of quadrotor unmanned aerial vehicle high‐accuracy trajectory tracking is considered in the paper of Pereida et al, where a new learning‐based adaptive approach is presented. The learning‐based controller combines a model‐based linear L 1 adaptive controller with a data‐driven iterative learning controller (ILC), to achieve good robust tracking performances.…”