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Mosskull, H. & Wahlberg, B. (2024). Adaptive feedforward control of sinusoidal disturbances with applications to electric propulsion systems. Control Engineering Practice, 146, Article ID 105892.
Open this publication in new window or tab >>Adaptive feedforward control of sinusoidal disturbances with applications to electric propulsion systems
2024 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 146, article id 105892Article in journal (Refereed) Published
Abstract [en]

Generalized adaptive feedforward cancellation with a reference sensor is considered to specifically suppress second harmonic torque oscillations with an ac fed propulsion system for an electric train. A single complex-valued design parameter is tracked through gradient-type adaptation. Both Cartesian and polar parameter representations are considered, resulting in quite varying convergence properties. Three different adaptation algorithms are proposed and evaluated using power lab experiments. At fixed operating conditions, a Cartesian form parameter adaptation is shown to be more robust to the choice of initial conditions, whereas a polar form representation shows better performance when covering a wide range of operating points.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Active noise control, Adaptive control, Power electronics, Transportation systems
National Category
Computational Mathematics
Identifiers
urn:nbn:se:kth:diva-344175 (URN)10.1016/j.conengprac.2024.105892 (DOI)001203428900001 ()2-s2.0-85185832537 (Scopus ID)
Note

QC 20240307

Available from: 2024-03-06 Created: 2024-03-06 Last updated: 2024-05-02Bibliographically approved
Li, Y., Wahlberg, B., Hu, X. & Xie, L. (2024). Inverse Kalman filtering problems for discrete-time systems. Automatica, 163, Article ID 111560.
Open this publication in new window or tab >>Inverse Kalman filtering problems for discrete-time systems
2024 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 163, article id 111560Article in journal (Refereed) Published
Abstract [en]

In this paper, several inverse Kalman filtering problems are addressed, where unknown parameters and/or inputs in a filtering model are reconstructed from observations of the posterior estimates that can be noisy or incomplete. In particular, duality in inverse filtering and inverse optimal control is studied. It is shown that identifiability and solvability of the inverse Kalman filtering is closely related to that of an inverse linear quadratic regulator (LQR). Covariance matrices of model uncertainties are estimated by solving a well-posed inverse LQR problem. Identifiability of the considered inverse filtering models is established and least squares estimators are designed to be statistically consistent. In addition, algorithms are proposed to reconstruct the unknown sensor parameters as well as raw sensor measurements. Effectiveness and efficiency of the proposed methods are illustrated by numerical simulations.

Place, publisher, year, edition, pages
Elsevier BV, 2024
Keywords
Duality principle, Inverse filtering, Kalman filter, Linear quadratic regulator, Statistical consistency
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343670 (URN)10.1016/j.automatica.2024.111560 (DOI)001180657200001 ()2-s2.0-85184659997 (Scopus ID)
Note

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-05Bibliographically approved
Li, Y., Wahlberg, B., Xie, L. & Hu, X. (2023). A Duality-Based Approach to Inverse Kalman Filtering. In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023: . Paper presented at 22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023 (pp. 10258-10263). Elsevier BV, 56
Open this publication in new window or tab >>A Duality-Based Approach to Inverse Kalman Filtering
2023 (English)In: 22nd IFAC World Congress Yokohama, Japan, July 9-14, 2023, Elsevier BV , 2023, Vol. 56, p. 10258-10263Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, the inverse Kalman filtering problem is addressed using a duality-based framework, where certain statistical properties of uncertainties in a dynamical model are recovered from observations of its posterior estimates. The duality relation in inverse filtering and inverse optimal control is established. It is shown that the inverse Kalman filtering problem can be solved using results from a well-posed inverse linear quadratic regulator. Identifiability of the considered inverse filtering model is proved and a unique covariance matrix is recovered by a least squares estimator, which is also shown to be statistically consistent. Effectiveness of the proposed methods is illustrated by numerical simulations.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Series
IFAC-PapersOnLine, ISSN 2405-8963 ; 56
Keywords
covariance estimation, duality, identifiability, Inverse filtering, Kalman filters
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343749 (URN)10.1016/j.ifacol.2023.10.908 (DOI)2-s2.0-85184654869 (Scopus ID)
Conference
22nd IFAC World Congress, Yokohama, Japan, Jul 9 2023 - Jul 14 2023
Note

QC 20240222

Part of ISBN 9781713872344

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-22Bibliographically approved
Pereira, G. C., Wahlberg, B., Pettersson, H. & Mårtensson, J. (2023). Adaptive reference aware MPC for lateral control of autonomous vehicles. Control Engineering Practice, 132, Article ID 105403.
Open this publication in new window or tab >>Adaptive reference aware MPC for lateral control of autonomous vehicles
2023 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 132, article id 105403Article in journal (Refereed) Published
Abstract [en]

This work addresses the design of a path tracking controller for autonomous vehicles. It reformulates the Reference Aware MPC in order to guarantee closed-loop stability, while maintaining a safe and comfortable ride, and minimizing wear and tear of vehicle components. Stability is proved via Lyapunov techniques. Furthermore, to adapt the response of the controller online while in operation, a novel model for the nonlinear curvature response of the vehicle is proposed. This model is estimated online by means of Kalman filtering. Both the proposed controller and curvature response model are evaluated with simulations and through experiments on a Scania construction truck, where the advantages to the previous state-of-the-art are highlighted and discussed.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Model predictive control, Stability, Adaptive, Automatic control, Autonomous vehicles
National Category
Control Engineering Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-323429 (URN)10.1016/j.conengprac.2022.105403 (DOI)000909858200001 ()2-s2.0-85144613717 (Scopus ID)
Note

QC 20230404

Available from: 2023-02-01 Created: 2023-02-01 Last updated: 2023-04-04Bibliographically approved
Zhang, J., Ju, Y., Wahlberg, B., Mu, B. & Chen, T. (2023). An Efficient Implementation for Bayesian Manifold Regularization Method. In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023: . Paper presented at 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023 (pp. 6223-6228). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>An Efficient Implementation for Bayesian Manifold Regularization Method
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2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6223-6228Conference paper, Published paper (Refereed)
Abstract [en]

When applying the Bayesian manifold regularization method to function estimation problem with manifold constraints, the direct implementation has computational complexity O(N3), where N is the number of input-output data measurements. This becomes particularly costly when N is large. In this paper, we propose a more efficient implementation based on the Kalman filter and smoother using a state-space model realization of the underlying Gaussian process. Moreover, we explore the sequentially semi-separable structure of the Laplacian matrix and the posterior covariance matrix. Our proposed implementation has computational complexity O(N) and thus can be applied to large data problems. We exemplify the effectiveness of our proposed implementation through numerical simulations.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Bayesian manifold regularization, Kalman filter and smoother, Sequentially semi-separable matrix
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343724 (URN)10.1109/CDC49753.2023.10383279 (DOI)2-s2.0-85184805765 (Scopus ID)
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

Part of proceedings ISBN 9798350301243

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-02-29Bibliographically approved
Lourenço, I., Bobu, A., Rojas, C. R. & Wahlberg, B. (2023). Diagnosing and Repairing Feature Representations Under Distribution Shifts. In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023: . Paper presented at 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023 (pp. 3638-3645). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Diagnosing and Repairing Feature Representations Under Distribution Shifts
2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 3638-3645Conference paper, Published paper (Refereed)
Abstract [en]

Robots have been increasingly better at doing tasks for humans by learning from their feedback, but still often suffer from model misalignment due to missing or incorrectly learned features. When the features the robot needs to learn to perform its task are missing or do not generalize well to new settings, the robot will not be able to learn the task the human wants and, even worse, may learn a completely different and undesired behavior. Prior work shows how the robot can detect when its representation is missing some feature and can, thus, ask the human to be taught about the new feature; however, these works do not differentiate between features that are completely missing and those that exist but do not generalize to new environments. In the latter case, the robot would detect misalignment and simply learn a new feature, leading to an arbitrarily growing feature representation that can, in turn, lead to spurious correlations and incorrect learning down the line. In this work, we propose separating the two sources of misalignment: we propose a framework for determining whether a feature the robot needs is incorrectly learned and does not generalize to new environment setups vs. is entirely missing from the robot's representation. Once we diagnose the source of error, we show how the human can initiate the realignment process for the model: if the feature is missing, we follow prior work for learning new features; however, if the feature exists but does not generalize, we use data augmentation to expand its training and, thus, complete the repair process. We demonstrate the proposed approach in experiments with a simulated 7DoF robot manipulator and physical human corrections.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-343725 (URN)10.1109/CDC49753.2023.10383644 (DOI)001166433803009 ()2-s2.0-85184801993 (Scopus ID)
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

QC 20240226

Part of ISBN 979-8-3503-0124-3

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-05Bibliographically approved
Oliveira, R. F., Nair, S. H. & Wahlberg, B. (2023). Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control. In: IV 2023: IEEE Intelligent Vehicles Symposium, Proceedings. Paper presented at 34th IEEE Intelligent Vehicles Symposium, IV 2023, Anchorage, United States of America, Jun 4 2023 - Jun 7 2023. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control
2023 (English)In: IV 2023: IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on Branch Model Predictive Control to deal with these challenges. The multi-modality is addressed by considering multiple future outcomes associated with different decisions taken by the human driver. The interactive nature of humans is considered by modeling them as reactive agents impacted by the actions of the autonomous vehicle. Finally, we consider a model developed in human neuroscience studies as a possible way of encoding the decision making process of human drivers. We present simulation results in various scenarios, showing the advantages of the proposed method and its ability to plan assertive maneuvers that convey intent to humans.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-335038 (URN)10.1109/IV55152.2023.10186633 (DOI)001042247300099 ()2-s2.0-85167991199 (Scopus ID)
Conference
34th IEEE Intelligent Vehicles Symposium, IV 2023, Anchorage, United States of America, Jun 4 2023 - Jun 7 2023
Note

Part of ISBN 9798350346916

QC 20230831

Available from: 2023-08-31 Created: 2023-08-31 Last updated: 2023-09-21Bibliographically approved
Li, Y., Hu, X., Wahlberg, B. & Xie, L. (2023). Inverse Kalman Filtering for Systems with Correlated Noises. In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023: . Paper presented at 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023 (pp. 3626-3631). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Inverse Kalman Filtering for Systems with Correlated Noises
2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 3626-3631Conference paper, Published paper (Refereed)
Abstract [en]

This paper focuses on two inverse problems of the Kalman filter in which the process and measurement noises are correlated. The unknown covariance matrix in a stochastic system is reconstructed from observations of its posterior beliefs. For the standard inverse Kalman filtering problem, a novel duality-based formulation is proposed, where a well-defined inverse optimal control (IOC) problem is solved instead. Identifiability of the underlying model is proved, and a least squares estimator is designed that is statistically consistent. The time-invariant case using the steady-state Kalman gain is further studied. Since this inverse problem is ill-posed, a canonical class of covariance matrices is constructed, which can be uniquely identified from the dataset with asymptotic convergence. Finally, the performances of the proposed methods are illustrated by numerical examples.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-343718 (URN)10.1109/CDC49753.2023.10383198 (DOI)001166433803007 ()2-s2.0-85184795327 (Scopus ID)
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

QC 20240226

Part of ISBN 9798350301243

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-03-26Bibliographically approved
Winqvist, R., Lourenço, I., Quinzan, F., Rojas, C. R. & Wahlberg, B. (2023). Optimal Transport for Correctional Learning. In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023: . Paper presented at 62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023 (pp. 6806-6812). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Optimal Transport for Correctional Learning
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2023 (English)In: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 6806-6812Conference paper, Published paper (Refereed)
Abstract [en]

The contribution of this paper is a generalized formulation of correctional learning using optimal transport, which is about how to optimally transport one mass distribution to another. Correctional learning is a framework developed to enhance the accuracy of parameter estimation processes by means of a teacher-student approach. In this framework, an expert agent, referred to as the teacher, modifies the data used by a learning agent, known as the student, to improve its estimation process. The objective of the teacher is to alter the data such that the student's estimation error is minimized, subject to a fixed intervention budget. Compared to existing formulations of correctional learning, our novel optimal transport approach provides several benefits. It allows for the estimation of more complex characteristics as well as the consideration of multiple intervention policies for the teacher. We evaluate our approach on two theoretical examples, and on a human-robot interaction application in which the teacher's role is to improve the robots performance in an inverse reinforcement learning setting.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
National Category
Learning
Identifiers
urn:nbn:se:kth:diva-343744 (URN)10.1109/CDC49753.2023.10384158 (DOI)001166433805090 ()2-s2.0-85184818101 (Scopus ID)
Conference
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Note

Part of ISBN 9798350301243

QC 20240222

Available from: 2024-02-22 Created: 2024-02-22 Last updated: 2024-04-08Bibliographically approved
Lourenço, I., Winqvist, R., Rojas, C. R. & Wahlberg, B. (2022). A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning. In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC): . Paper presented at IEEE 61st Conference on Decision and Control (CDC), DEC 06-09, 2022, Cancun, MEXICO (pp. 3456-3461). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>A Teacher-Student Markov Decision Process-based Framework for Online Correctional Learning
2022 (English)In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 3456-3461Conference paper, Published paper (Refereed)
Abstract [en]

A classical learning setting typically concerns an agent/student who collects data, or observations, from a system in order to estimate a certain property of interest. Correctional learning is a type of cooperative teacher-student framework where a teacher, who has partial knowledge about the system, has the ability to observe and alter (correct) the observations received by the student in order to improve the accuracy of its estimate. In this paper, we show how the variance of the estimate of the student can be reduced with the help of the teacher. We formulate the corresponding online problem - where the teacher has to decide, at each time instant, whether or not to change the observations due to a limited budget - as a Markov decision process, from which the optimal policy is derived using dynamic programming. We validate the framework in numerical experiments, and compare the optimal online policy with the one from the batch setting.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-326382 (URN)10.1109/CDC51059.2022.9992858 (DOI)000948128102137 ()2-s2.0-85147008847 (Scopus ID)
Conference
IEEE 61st Conference on Decision and Control (CDC), DEC 06-09, 2022, Cancun, MEXICO
Note

QC 20230503

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2023-05-03Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1927-1690

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