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Yoo, Jaehyun
Publications (9 of 9) Show all publications
Lee, S. J., Yoo, J. & Kim, H. J. (2018). Design, Modeling and Control of T-3-Multirotor: a Tilting Thruster Type Multirotor. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 1214-1219). IEEE Computer Society
Open this publication in new window or tab >>Design, Modeling and Control of T-3-Multirotor: a Tilting Thruster Type Multirotor
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE Computer Society, 2018, p. 1214-1219Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a new design of multirotor, named as 'Tilting Thruster Type' (T-3)-multirotor. The new platform is equipped with mechanically separated thrusters, which can take any fuselage posture within a specified range regardless of any direction of translational acceleration. A specially designed servo-linkage mechanism is employed for relative attitude control between the thruster and the fuselage. Mathematical modeling and analysis of the new platform are conducted to explore the control method of the dynamically complex system. For demonstrating the potential of the new T(3-)multirotor, an autonomous level flight is performed where the fuselage maintains zero roll and pitch angle during the entire flight. Both simulation and experimental results are provided with detailed analysis.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Aerospace Engineering
Identifiers
urn:nbn:se:kth:diva-237159 (URN)000446394501004 ()978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2019-08-20Bibliographically approved
Yoo, J., Nekouei, E. & Johansson, K. H. (2018). Event-based Observer and MPC with Disturbance Attenuation using ERM Learning. In: 2018 European Control Conference, ECC 2018: . Paper presented at 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018 (pp. 1894-1899). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8550289.
Open this publication in new window or tab >>Event-based Observer and MPC with Disturbance Attenuation using ERM Learning
2018 (English)In: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1894-1899, article id 8550289Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a learning-based approach for disturbance attenuation for a non-linear dynamical system with event-based observer and model predictive control (MPC). Using the empirical risk minimization (ERM) method, we can obtain a learning error bound which is function of the number of samples, learning parameters, and model complexity. It enables us to analyze the closed-loop stability in terms of the learning property, where the state estimation error by the ERM learning is guaranteed to be bounded. Simulation results underline the learning's capability, the control performance and the event-triggering efficiency in comparison to the conventional event-triggered control scheme.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-241510 (URN)10.23919/ECC.2018.8550289 (DOI)2-s2.0-85059811243 (Scopus ID)9783952426982 (ISBN)
Conference
16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018
Note

QC 20190123

Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2019-11-14Bibliographically approved
Yoo, J., Molin, A., Jafarian, M., Esen, H., Dimarogonas, D. V. & Johansson, K. H. (2017). Event-triggered Model Predictive Control with Machine Learning for Compensation of Model Uncertainties. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017: . Paper presented at IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia (pp. 5463-5468). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Event-triggered Model Predictive Control with Machine Learning for Compensation of Model Uncertainties
Show others...
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5463-5468Conference paper, Published paper (Refereed)
Abstract [en]

As one of the extensions of model predictive control (MPC), event-triggered MPC takes advantage of the reduction of control updates. However, approaches to event-triggered MPCs may be subject to frequent event-triggering instants in the presence of large disturbances. Motivated by this, this paper suggests an application of machine learning to this control method in order to learn a compensation model for disturbance attenuation. The suggested method improves both event-triggering policy efficiency and control accuracy compared to previous approaches to event-triggered MPCs. We employ the radial basis function (RBF) kernel based machine learning technique. By the universial approximation property of the RBF, which imposes an upper bound on the training error, we can present the stability analysis of the learningaided control system. The proposed algorithm is evaluated by means of position control of a nonholonomic robot subject to state-dependent disturbances. Simulation results show that the developed method yields not only two times less event triggering instants, but also improved tracking performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-223877 (URN)10.1109/CDC.2017.8264468 (DOI)000424696905039 ()2-s2.0-85046154893 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia
Funder
Knut and Alice Wallenberg FoundationSwedish Research CouncilSwedish Foundation for Strategic Research
Note

QC 20180305

Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-06-04Bibliographically approved
Yoo, J., Johansson, K. H. & Kim, H. J. (2017). Indoor Localization Without a Prior Map by Trajectory Learning From Crowdsourced Measurements. IEEE Transactions on Instrumentation and Measurement, 66(11), 2825-2835
Open this publication in new window or tab >>Indoor Localization Without a Prior Map by Trajectory Learning From Crowdsourced Measurements
2017 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 66, no 11, p. 2825-2835Article in journal (Refereed) Published
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-216596 (URN)10.1109/TIM.2017.2729438 (DOI)000412573300004 ()2-s2.0-85028996599 (Scopus ID)
Note

QC 20171116

Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2017-11-16Bibliographically approved
Yoo, J. & Johansson, K. H. (2017). Learning communication delay patterns for remotely controlled UAV networks. In: IFAC PAPERSONLINE: . Paper presented at 20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE (pp. 13216-13221). Elsevier, 50(1)
Open this publication in new window or tab >>Learning communication delay patterns for remotely controlled UAV networks
2017 (English)In: IFAC PAPERSONLINE, Elsevier, 2017, Vol. 50, no 1, p. 13216-13221Conference paper, Published paper (Refereed)
Abstract [en]

This paper deals with collaborative unmanned aerial vehicles (UAVs) that are remotely controlled from a cloud server. The main contribution is to apply machine learning technique to find a pattern of network-induced effects on maneuvers of UAVs, in order to compensate for time delays and packet losses in remote communication. As machine learning technique, a Gaussian process (GP) based approach is employed due to its computational simplicity and flexibility in modelling complex expressions using a small number of parameters. We combine a deterministic compensation for an enhanced GP model to overcome a problem of the lack of training data at the beginning of training phase. This is done by defining training data input as a set of delayed observation and the deterministic compensation term, and by training the GP on residual between the true state and the input set. The proposed algorithm is evaluated to collaborative trajectory tracking of two UAVs. Simulations are performed for various delays and tracking scenarios. It is shown that the better tracking results are achieved compared to a conventional linear compensation algorithm.

Place, publisher, year, edition, pages
Elsevier, 2017
Series
IFAC PAPERSONLINE, ISSN 2405-8963 ; 50
Keywords
Networked robotics, unmanned aerial vehicles, time-delay systems, machine learning
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-243586 (URN)10.1016/j.ifacol.2017.08.1954 (DOI)000423965200194 ()2-s2.0-85044252181 (Scopus ID)
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE
Note

QC 20190206

Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-02-06Bibliographically approved
Yoo, J., Kim, H. J. & Johansson, K. H. (2017). Path planning for remotely controlled UAVs using Gaussian process filter. In: 17th International Conference on Control, Automation and Systems, ICCAS 2017: . Paper presented at 17th International Conference on Control, Automation and Systems, ICCAS 2017, Ramada PlazaJeju, South Korea, 18 October 2017 through 21 October 2017 (pp. 477-482). IEEE Computer Society
Open this publication in new window or tab >>Path planning for remotely controlled UAVs using Gaussian process filter
2017 (English)In: 17th International Conference on Control, Automation and Systems, ICCAS 2017, IEEE Computer Society, 2017, p. 477-482Conference paper, Published paper (Refereed)
Abstract [en]

Most of the recent results in control of unmanned aerial vehicles (UAVs) have focused on motion stability and navigation in well-structured environments, without considering communication delay influences. In order to deal with time delays and packet losses in networked UAVs, this paper suggests a machine learning based Gaussian process (GP) filter for a path planning problem. The developed GP filter estimates the UAV states accurately given delayed observation by learning the pattern of network-induced effects on UAV maneuvers. We validate that the GP filter produces the lower error rate than Kalman filter by analyzing error covariances. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two networked-UAVs and the better control performance is achieved.

Place, publisher, year, edition, pages
IEEE Computer Society, 2017
Series
International Conference on Control Automation and Systems, ISSN 2093-7121
Keywords
Filtering, Gaussian process for regression, Machine learning, Model predictive control, Path planning
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-225502 (URN)10.23919/ICCAS.2017.8204486 (DOI)000426974400076 ()2-s2.0-85044448519 (Scopus ID)9788993215137 (ISBN)
Conference
17th International Conference on Control, Automation and Systems, ICCAS 2017, Ramada PlazaJeju, South Korea, 18 October 2017 through 21 October 2017
Funder
Swedish Foundation for Strategic Research Swedish Research CouncilKnut and Alice Wallenberg Foundation
Note

QC 20180406

Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2019-11-14Bibliographically approved
Yoo, J. & Johansson, K. H. (2017). Semi-Supervised Learning for Mobile Robot Localization using Wireless Signal Strengths. In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN): . Paper presented at 8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN.
Open this publication in new window or tab >>Semi-Supervised Learning for Mobile Robot Localization using Wireless Signal Strengths
2017 (English)In: 2017 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes a new semi-supervised machine learning for localization. It improves localization efficiency by reducing efforts needed to calibrate labeled training data by using unlabeled data, where training data come from received signal strengths of a wireless communication link. The main idea is to treat training data as spatio-temporal data. We compare the proposed algorithm with the state-of-art semi-supervised learning methods. The algorithms are evaluated for estimating the unknown location of a smartphone mobile robot. The experimental results show that the developed learning algorithm is the most accurate and robust to the varying amount of training data, without sacrificing the computation speed.

Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-220652 (URN)000417415600060 ()978-1-5090-6299-7 (ISBN)
Conference
8th International Conference on Indoor Positioning and Indoor Navigation (IPIN), SEP 18-21, 2017, Sapporo, JAPAN
Note

QC 20180111

Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-03-12Bibliographically approved
Yoo, J., Lee, S., Kim, H. J. & Johansson, K. H. (2017). Trajectory generation for networked UAVs using online learning for delay compensation. In: 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017: . Paper presented at 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, Mauna Lani Bay HotelKohala Coast, United States, 27 August 2017 through 30 August 2017 (pp. 1941-1946). IEEE
Open this publication in new window or tab >>Trajectory generation for networked UAVs using online learning for delay compensation
2017 (English)In: 1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, IEEE, 2017, p. 1941-1946Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data. The proposed algorithm is evaluated on a collaborative trajectory tracking task for two UAVs. Improved tracking is achieved in comparison to a conventional linear compensation algorithm.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-228178 (URN)10.1109/CCTA.2017.8062740 (DOI)000426981500310 ()2-s2.0-85047635778 (Scopus ID)9781509021826 (ISBN)
Conference
1st Annual IEEE Conference on Control Technology and Applications, CCTA 2017, Mauna Lani Bay HotelKohala Coast, United States, 27 August 2017 through 30 August 2017
Note

QC 20180521

Available from: 2018-05-21 Created: 2018-05-21 Last updated: 2018-06-12Bibliographically approved
Yoo, J., Kim, H. J. & Johansson, K. H. (2016). Mapless Indoor Localization by Trajectory Learning from a Crowd. In: 2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN): . Paper presented at International Conference on Indoor Positioning and Indoor Navigation (IPIN), OCT 04-07, 2016, Madrid, SPAIN. Institute of Electrical and Electronics Engineers (IEEE), Article ID 7743685.
Open this publication in new window or tab >>Mapless Indoor Localization by Trajectory Learning from a Crowd
2016 (English)In: 2016 INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN), Institute of Electrical and Electronics Engineers (IEEE), 2016, article id 7743685Conference paper, Published paper (Refereed)
Abstract [en]

This paper suggests a mapless indoor localization using wifi received signal strength (RSS) of a smartphone, collected by multiple people. A new trajectory learning algorithm by combining a dynamic time warping and a machine learning technique is proposed in order to generate an alternative map. Moreover, we combine particle filter and Gaussian process (GP) for the position estimation, because it can use the alternative map as the probabilistic function (the prior), and can use probabilistic relationship (the likelihood) between wifi RSSs and location. Field experimental results confirm the usefulness of our algorithm when the map is not available and robustness against outliers, in that the accuracy of the proposed localization is similar to that using the true map information.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
Keywords
Artificial intelligence, Learning algorithms, Learning systems, Wireless local area networks (WLAN), Dynamic time warping, Gaussian process, Indoor localization, Machine learning techniques, Multiple people, Position estimation, Probabilistic functions, Wifi received signal strengths
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-199802 (URN)10.1109/IPIN.2016.7743685 (DOI)000390141300104 ()2-s2.0-85004000531 (Scopus ID)978-1-5090-2425-4 (ISBN)
Conference
International Conference on Indoor Positioning and Indoor Navigation (IPIN), OCT 04-07, 2016, Madrid, SPAIN
Note

QC 20170119

Available from: 2017-01-19 Created: 2017-01-16 Last updated: 2017-01-19Bibliographically approved
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