A future concern of iterative learning control : A survey

Riaz, Saleem; Hui, Lin; Aldemir, Mehmet Şerif; Afzal, Farkhanda

doi:10.1080/09720510.2020.1856421

Cited by 7 publications

(3 citation statements)

References 84 publications

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“…Theorem 1. Under Assumptions 1,2, suppose a drone model satisfies (5), and the controller is designed as (9), with (11), (15) and (16).…”

Section: B Spatial Ilc Designmentioning

confidence: 99%

“…This further implies ∇ ∥e p (l)∥ = 0, l ∈ [0, L]. In this case, e p = x th /k p according to (16). The error is decomposed into two components…”

Section: B Spatial Ilc Designmentioning

confidence: 99%

“…With ILC, the tracking error in the last iteration is used to correct the unsatisfactory control command, thereby improving the tracking accuracy of the system iteratively [15]. However, the existing ILC is only used for a specific trajectory tracking problem [16] and cannot be directly applied to drone races.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

Gao

Che

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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It is often necessary for drones to complete delivery, photography, and rescue in the shortest time to increase efficiency. Many autonomous drone races provide platforms to pursue algorithms to finish races as quickly as possible for the above purpose. Unfortunately, existing methods often fail to keep training and racing time short in drone racing competitions. This motivates us to develop a high-efficient learning method by imitating the training experience of top racing drivers. Unlike traditional iterative learning control methods for accurate tracking, the proposed approach iteratively learns a trajectory online to finish the race as quickly as possible. Simulations and experiments using different models show that the proposed approach is model-free and is able to achieve the optimal result with low computation requirements. Furthermore, this approach surpasses some state-of-the-art methods in racing time on a benchmark drone racing platform. An experiment on a real quadcopter is also performed to demonstrate its effectiveness.

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“…Theorem 1. Under Assumptions 1,2, suppose a drone model satisfies (5), and the controller is designed as (9), with (11), (15) and (16).…”

Section: B Spatial Ilc Designmentioning

confidence: 99%

“…This further implies ∇ ∥e p (l)∥ = 0, l ∈ [0, L]. In this case, e p = x th /k p according to (16). The error is decomposed into two components…”

Section: B Spatial Ilc Designmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

Gao

Che

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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An improved fast error convergence topology for PD^α-type fractional-order ILC

Riaz

Hui

Mahsud

et al. 2021

Journal of Interdisciplinary Mathematics

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High level browser security in cloud computing services from cross site scripting attacks

Malik

Kumar

et al. 2022

Journal of Discrete Mathematical Sciences and Cryptography

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A future concern of iterative learning control : A survey

Cited by 7 publications

References 84 publications

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

An improved fast error convergence topology for PD^α-type fractional-order ILC

High level browser security in cloud computing services from cross site scripting attacks

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A future concern of iterative learning control : A survey

Cited by 7 publications

References 84 publications

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

Autonomous Drone Racing: Time-Optimal Spatial Iterative Learning Control within a Virtual Tube

An improved fast error convergence topology for PDα-type fractional-order ILC

High level browser security in cloud computing services from cross site scripting attacks

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Product

Resources

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An improved fast error convergence topology for PD^α-type fractional-order ILC