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Zeitschriftenartikel zum Thema „LPV/quasi-LPV Systems“

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Veröffentlicht am 1. Juni 2024

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1

Wang, Xiao Ming, Alois Steiner und Jan Fiala. „Linear Parameter-Varying Modeling of Electric Vehicle Air Conditioning System“. Applied Mechanics and Materials 148-149 (Dezember 2011): 318–25. http://dx.doi.org/10.4028/www.scientific.net/amm.148-149.318.

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This article presents the approach of quasi LPV (Linear Parameter-Varying) modeling techniques for an air conditioning system of an electric vehicle. Vehicle air conditioning systems are strongly non-linear systems and it is a challenging task to get a precise real time model for control purposes. Therefore, an LPV method is first introduced to estimate the air conditioning system. Experimental results show that the LPV model delivers a very high accuracy for the COP (Coefficient Of Performance) estimation, that can’t be reached by traditional identification methods. Some discussion about the model structure and its application are presented and a non-linear LPV model structure similar to the Hammerstein structure is proposed.

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2

Pérez-Estrada, A. J., G. L. Osorio-Gordillo, M. Darouach und V. H. Olivares-Peregrino. „Generalized dynamic observer design for quasi-LPV systems“. at - Automatisierungstechnik 66, Nr. 3 (26.03.2018): 225–33. http://dx.doi.org/10.1515/auto-2017-0060.

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Abstract This paper presents a new generalized dynamic observer (GDO) for quasi-linear parameter varying (LPV) systems. It generalises the structures of the proportional observer (PO) and proportional integral observer (PIO). The design of the GDO is derived from the solution of linear matrix inequalities (LMIs) and the solution of the algebraic constraints obtained from the estimation error analysis. The efficiency of the proposed approach is illustrated by a numerical example.

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3

Lu, Yaohui, und Yaman Arkun. „Quasi-Min-Max MPC algorithms for LPV systems“. Automatica 36, Nr. 4 (April 2000): 527–40. http://dx.doi.org/10.1016/s0005-1098(99)00176-4.

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4

Grimble, M. J., Pawel Majecki und M. R. Katebi. „Extended NGMV Predictive Control of Quasi-LPV Systems“. IFAC-PapersOnLine 50, Nr. 1 (Juli 2017): 4101–7. http://dx.doi.org/10.1016/j.ifacol.2017.08.795.

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5

Robles, Ruben, Antonio Sala und Miguel Bernal. „Performance-oriented quasi-LPV modeling of nonlinear systems“. International Journal of Robust and Nonlinear Control 29, Nr. 5 (21.12.2018): 1230–48. http://dx.doi.org/10.1002/rnc.4444.

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6

Oehlschlägel, Thimo, Christian Heise, Stephan Theil und Thomas Steffen. „Stability Analysis of Closed Loops of non-linear Systems and LPV Controllers designed using approximated Quasi-LPV Systems“. IFAC Proceedings Volumes 45, Nr. 13 (2012): 349–54. http://dx.doi.org/10.3182/20120620-3-dk-2025.00165.

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7

Polat, İ., İ. E. Köse und E. Eşkinat. „Dynamic output feedback control of quasi-LPV mechanical systems“. IET Control Theory & Applications 1, Nr. 4 (01.07.2007): 1114–21. http://dx.doi.org/10.1049/iet-cta:20060326.

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8

Coutinho, Pedro H. S., Márcia L. C. Peixoto, Miguel Bernal, Anh-Tu Nguyen und Reinaldo M. Palhares. „Local Sampled-Data Gain-Scheduling Control of quasi-LPV Systems“. IFAC-PapersOnLine 54, Nr. 4 (2021): 86–91. http://dx.doi.org/10.1016/j.ifacol.2021.10.015.

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9

Huang, Hua, De Feng He und Qiu Xia Chen. „Quasi-min-max dynamic output feedback MPC for LPV systems“. International Journal of System Control and Information Processing 1, Nr. 3 (2014): 233. http://dx.doi.org/10.1504/ijscip.2014.059675.

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10

Arezki, H., A. Alessandri und A. Zemouche. „Robust Moving-Horizon Estimation for Quasi-LPV Discrete-Time Systems*“. IFAC-PapersOnLine 56, Nr. 2 (2023): 6771–76. http://dx.doi.org/10.1016/j.ifacol.2023.10.384.

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11

Machala, Dawid, Simona Dobre, Marie Albisser, Floriane Collin und Marion Gilson. „Quasi-LPV modelling of a projectile’s behaviour in flight“. IFAC-PapersOnLine 51, Nr. 15 (2018): 1080–85. http://dx.doi.org/10.1016/j.ifacol.2018.09.050.

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12

Ding, Baocang, Xubin Ping und Hongguang Pan. „On dynamic output feedback robust MPC for constrained quasi-LPV systems“. International Journal of Control 86, Nr. 12 (Dezember 2013): 2215–27. http://dx.doi.org/10.1080/00207179.2013.809796.

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13

Pérez-Estrada, A. J., G. L. Osorio-Gordillo, M. Darouach, M. Alma und V. H. Olivares-Peregrino. „Generalized dynamic observers for quasi-LPV systems with unmeasurable scheduling functions“. International Journal of Robust and Nonlinear Control 28, Nr. 17 (27.08.2018): 5262–78. http://dx.doi.org/10.1002/rnc.4309.

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14

López-Estrada, Francisco-Ronay, Damiano Rotondo und Guillermo Valencia-Palomo. „A Review of Convex Approaches for Control, Observation and Safety of Linear Parameter Varying and Takagi-Sugeno Systems“. Processes 7, Nr. 11 (04.11.2019): 814. http://dx.doi.org/10.3390/pr7110814.

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This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).

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15

Degtyarev, G. L., R. N. Faizutdinov und I. O. Spiridonov. „Multiobjective Robust Controller Synthesis for Nonlinear Mechanical System“. Mekhatronika, Avtomatizatsiya, Upravlenie 19, Nr. 11 (08.11.2018): 691–98. http://dx.doi.org/10.17587/mau.19.691-698.

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In the paper multiobjective robust controller synthesis problem for nonlinear mechanical system described by Lagrange’s equations of the second kind is considered. Such tasks have numerous practical applications, for example in controller design of robotic systems and gyro-stabilized platforms. In practice, we often have to use uncertain mathematical plant models in controller design. Therefore, ensuring robustness in presence of parameters perturbations and unknown external disturbances is an important requirement for designed systems. Much of modern robust control theory is linear. When the actual system exhibits nonlinear behavior, nonlinearities are usually included in the uncertainty set of the plant. A disadvantage of this approach is that resulting controllers may be too conservative especially when nonlinearities are significant. The nonlinear H∞ optimal control theory developed on the basis of differential game theory is a natural extension of the linear robust control theory. Nonlinear theory methods ensure robust stability of designed control systems. However, to determine nonlinear H∞-control law, the partial differential equation have to be solved which is a rather complicated task. In addition, it is difficult to ensure robust performance of controlled processes when using this method. In this paper, methods of linear parameter-varying (LPV) systems are used to synthesize robust control law. It is shown, that Lagrange system may be adequately represented in the form of quasi-LPV model. From the computational point of view, the synthesis procedure is reduced to convex optimization techniques under constraints expressed in the form of linear matrix inequalities (LMIs). Measured parameters are incorporated in the control law, thus ensuring continuous adjustment of the controller parameters to the current plant dynamics and better performance of control processes in comparison with H∞-regulators. Furthermore, the use of the LMIs allows to take into account the transient performance requirements in the controller synthesis. Since the quasi-LPV system depends continuously on the parameter vector, the LMI system is infinite-dimensional. This infinitedimensional system is reduced to a finite set of LMIs by introducing a polytopic LPV representation. The example of multiobjective robust control synthesis for electro-optical device’s line of sight pointing and stabilization system suspended in two-axes inertially stabilized platform is given.

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16

Cisneros, Pablo S. G., Adwait Datar, Patrick Göttsch und Herbert Werner. „Data-Driven quasi-LPV Model Predictive Control Using Koopman Operator Techniques“. IFAC-PapersOnLine 53, Nr. 2 (2020): 6062–68. http://dx.doi.org/10.1016/j.ifacol.2020.12.1676.

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17

Cisneros, Pablo S. G., und Herbert Werner. „Wide Range Stabilization of a Pendubot using quasi-LPV Predictive Control“. IFAC-PapersOnLine 52, Nr. 28 (2019): 164–69. http://dx.doi.org/10.1016/j.ifacol.2019.12.367.

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18

Cisneros, Pablo S. G., Aadithyan Sridharan und Herbert Werner. „Constrained Predictive Control of a Robotic Manipulator using quasi-LPV Representations“. IFAC-PapersOnLine 51, Nr. 26 (2018): 118–23. http://dx.doi.org/10.1016/j.ifacol.2018.11.158.

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19

Grimble, Mike J., und Pawel Majecki. „Observer Based Restricted Structure Generalized Predictive Control for quasi-LPV Nonlinear Systems“. IFAC-PapersOnLine 53, Nr. 2 (2020): 4264–71. http://dx.doi.org/10.1016/j.ifacol.2020.12.2480.

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20

Alessandri, A., M. Zasadzinski und A. Zemouche. „Optimistic vs Pessimistic Moving-Horizon Estimation for Quasi–LPV Discrete-Time Systems“. IFAC-PapersOnLine 53, Nr. 2 (2020): 5004–9. http://dx.doi.org/10.1016/j.ifacol.2020.12.1096.

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21

Meng Zhao, Canchen Jiang, Xiaoming Tang und Minghong She. „Interpolation Model Predictive Control of Nonlinear Systems Described by Quasi-LPV Model“. Automatic Control and Computer Sciences 52, Nr. 5 (September 2018): 354–64. http://dx.doi.org/10.3103/s0146411618050085.

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22

Coutinho, Pedro H. S., Márcia L. C. Peixoto, Iury Bessa und Reinaldo Martinez Palhares. „Dynamic event-triggered gain-scheduling control of discrete-time quasi-LPV systems“. Automatica 141 (Juli 2022): 110292. http://dx.doi.org/10.1016/j.automatica.2022.110292.

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23

Dehak, Amine, Anh-Tu Nguyen, Antoine Dequidt, Laurent Vermeiren und Michel Dambrine. „A Reduced-Complexity Polytopic Control Approach for Uncertain quasi-LPV Descriptor Systems“. IFAC-PapersOnLine 56, Nr. 2 (2023): 2176–81. http://dx.doi.org/10.1016/j.ifacol.2023.10.1124.

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24

Goyal, Jitendra Kumar, Shubham Aggarwal, Sandip Ghosh, Shyam Kamal und Pawel Dworak. „Quasi‐LPV PI control of TRMS subject to actuator saturation“. IET Control Theory & Applications 14, Nr. 19 (Dezember 2020): 3157–67. http://dx.doi.org/10.1049/iet-cta.2020.0361.

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25

Ping, Xubin, und Ning Sun. „Dynamic Output Feedback Robust Model Predictive Control via Zonotopic Set-Membership Estimation for Constrained Quasi-LPV Systems“. Journal of Applied Mathematics 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/875850.

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For the quasi-linear parameter varying (quasi-LPV) system with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) is investigated. The estimation error set is represented by a zonotope and refreshed by the zonotopic set-membership estimation method. By properly refreshing the estimation error set online, the bounds of true state at the next sampling time can be obtained. Furthermore, the feasibility of the main optimization problem at the next sampling time can be determined at the current time. A numerical example is given to illustrate the effectiveness of the approach.

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26

Coutinho, Pedro Henrique Silva, und Reinaldo Martínez Palhares. „Dynamic periodic event-triggered gain-scheduling control co-design for quasi-LPV systems“. Nonlinear Analysis: Hybrid Systems 41 (August 2021): 101044. http://dx.doi.org/10.1016/j.nahs.2021.101044.

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27

Rahmanian, Farnoosh, und Mohammad Hassan Asemani. „Quasi-LPV positive observer-based control of closed-loop deep brain stimulation systems“. Biomedical Signal Processing and Control 86 (September 2023): 105238. http://dx.doi.org/10.1016/j.bspc.2023.105238.

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28

Kim, Tae-Hyoung, und Ho-Woon Lee. „Quasi-min-max output-feedback model predictive control for LPV systems with input saturation“. International Journal of Control, Automation and Systems 15, Nr. 3 (22.05.2017): 1069–76. http://dx.doi.org/10.1007/s12555-016-0378-y.

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29

Bianchi, F. D., R. J. Mantz und C. F. Christiansen. „Gain scheduling control of variable-speed wind energy conversion systems using quasi-LPV models“. Control Engineering Practice 13, Nr. 2 (Februar 2005): 247–55. http://dx.doi.org/10.1016/j.conengprac.2004.03.006.

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30

Park, Jee-Hun, Tae-Hyoung Kim und Toshiharu Sugie. „Output feedback model predictive control for LPV systems based on quasi-min–max algorithm“. Automatica 47, Nr. 9 (September 2011): 2052–58. http://dx.doi.org/10.1016/j.automatica.2011.06.015.

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31

Rotondo, Damiano, Fatiha Nejjari und Vicenç Puig. „Quasi-LPV modeling, identification and control of a twin rotor MIMO system“. Control Engineering Practice 21, Nr. 6 (Juni 2013): 829–46. http://dx.doi.org/10.1016/j.conengprac.2013.02.004.

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32

Cisneros, Pablo Gonzalez, und Herbert Werner. „Fast Nonlinear MPC for Reference Tracking Subject to Nonlinear Constraints via Quasi-LPV Representations“. IFAC-PapersOnLine 50, Nr. 1 (Juli 2017): 11601–6. http://dx.doi.org/10.1016/j.ifacol.2017.08.1650.

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33

Hu, Jianchen, und Baocang Ding. „An off-line output feedback MPC strategy for nonlinear systems represented by quasi-LPV model“. IFAC-PapersOnLine 51, Nr. 20 (2018): 66–71. http://dx.doi.org/10.1016/j.ifacol.2018.10.176.

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34

Fouka, Majda, Chouki Sentouh und Jean-Christophe Popieul. „Quasi-LPV Interconnected Observer Design for Full Vehicle Dynamics Estimation With Hardware Experiments“. IEEE/ASME Transactions on Mechatronics 26, Nr. 4 (August 2021): 1763–72. http://dx.doi.org/10.1109/tmech.2021.3074743.

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35

Xiong, Junfeng, Decai Li, Yuqing He, Feng Gu und Jianda Han. „Active Quasi-LPV Modeling and Identification for a Water-Jet Propulsion USV: An Experimental Study“. IFAC-PapersOnLine 48, Nr. 28 (2015): 1359–64. http://dx.doi.org/10.1016/j.ifacol.2015.12.321.

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36

Rotondo, Damiano, Fatiha Nejjari und Vicenç Puig. „Robust Quasi–LPV Model Reference FTC of a Quadrotor Uav Subject to Actuator Faults“. International Journal of Applied Mathematics and Computer Science 25, Nr. 1 (01.03.2015): 7–22. http://dx.doi.org/10.1515/amcs-2015-0001.

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Abstract A solution for fault tolerant control (FTC) of a quadrotor unmanned aerial vehicle (UAV) is proposed. It relies on model reference-based control, where a reference model generates the desired trajectory. Depending on the type of reference model used for generating the reference trajectory, and on the assumptions about the availability and uncertainty of fault estimation, different error models are obtained. These error models are suitable for passive FTC, active FTC and hybrid FTC, the latter being able to merge the benefits of active and passive FTC while reducing their respective drawbacks. The controller is generated using results from the robust linear parameter varying (LPV) polytopic framework, where the vector of varying parameters is used to schedule between uncertain linear time invariant (LTI) systems. The design procedure relies on solving a set of linear matrix inequalities (LMIs) in order to achieve regional pole placement and H∞ norm bounding constraints. Simulation results are used to compare the different FTC strategies.

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37

Su, Yang, und Kok Kiong Tan. „Comments on “Output feedback model predictive control for LPV systems based on quasi-min–max algorithm”“. Automatica 48, Nr. 9 (September 2012): 2385. http://dx.doi.org/10.1016/j.automatica.2012.06.100.

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38

Gómez-Peñate, S., F. R. López-Estrada, G. Valencia-Palomo, D. Rotondo und J. Enríquez-Zárate. „Actuator and sensor fault estimation based on a proportional-integral quasi-LPV observer with inexact scheduling parameters“. IFAC-PapersOnLine 52, Nr. 28 (2019): 100–105. http://dx.doi.org/10.1016/j.ifacol.2019.12.355.

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39

Yassuda, Julio Yuzo, Cristiano Marcos Agulhari und Emerson Ravazzi Pires da Silva. „Sampled-data robust control of a 2-DoF helicopter modeled using a quasi-LPV framework“. Control Engineering Practice 145 (April 2024): 105870. http://dx.doi.org/10.1016/j.conengprac.2024.105870.

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40

Mattei, Massimiliano. „An LPV approach to the robust control of a class of quasi-linear propagation processes“. Journal of Process Control 14, Nr. 6 (September 2004): 651–60. http://dx.doi.org/10.1016/j.jprocont.2004.01.001.

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41

Ping, Xubin, Bo Qian und Ning Sun. „Dynamic Output Feedback Robust MPC with Input Saturation Based on Zonotopic Set-Membership Estimation“. Mathematical Problems in Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5292375.

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For quasi-linear parameter varying (quasi-LPV) systems with bounded disturbance, a synthesis approach of dynamic output feedback robust model predictive control (OFRMPC) with the consideration of input saturation is investigated. The saturated dynamic output feedback controller is represented by a convex hull involving the actual dynamic output controller and an introduced auxiliary controller. By taking both the actual output feedback controller and the auxiliary controller with a parameter-dependent form, the main optimization problem can be formulated as convex optimization. The consideration of input saturation in the main optimization problem reduces the conservatism of dynamic output feedback controller design. The estimation error set and bounded disturbance are represented by zonotopes and refreshed by zonotopic set-membership estimation. Compared with the previous results, the proposed algorithm can not only guarantee the recursive feasibility of the optimization problem, but also improve the control performance at the cost of higher computational burden. A nonlinear continuous stirred tank reactor (CSTR) example is given to illustrate the effectiveness of the approach.

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42

Inoue, Roberto S., Adriano A. G. Siqueira und Marco H. Terra. „Experimental results on the nonlinear∞control via quasi-LPV representation and game theory for wheeled mobile robots“. Robotica 27, Nr. 4 (Juli 2009): 547–53. http://dx.doi.org/10.1017/s0263574708004931.

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SUMMARYIn this paper, nonlinear dynamic equations of a wheeled mobile robot are described in the state-space form where the parameters are part of the state (angular velocities of the wheels). This representation, known as quasi-linear parameter varying, is useful for control designs based on nonlinear∞approaches. Two nonlinear∞controllers that guarantee induced2-norm, between input (disturbances) and output signals, bounded by an attenuation level γ, are used to control a wheeled mobile robot. These controllers are solved via linear matrix inequalities and algebraic Riccati equation. Experimental results are presented, with a comparative study among these robust control strategies and the standard computed torque, plus proportional-derivative, controller.

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43

Wei, Xiukun, und Luigi Del Re. „ON PERSISTENT EXCITATION FOR PARAMETER ESTIMATION OF QUASI-LPV SYSTEMS AND ITS APPLICATION IN MODELING OF DIESEL ENGINE TORQUE“. IFAC Proceedings Volumes 39, Nr. 1 (2006): 517–22. http://dx.doi.org/10.3182/20060329-3-au-2901.00079.

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44

Srinivasarengan, Krishnan, José Ragot, Christophe Aubrun und Didier Maquin. „An adaptive observer design approach for a class of discrete-time nonlinear systems“. International Journal of Applied Mathematics and Computer Science 28, Nr. 1 (01.03.2018): 55–67. http://dx.doi.org/10.2478/amcs-2018-0004.

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AbstractWe consider the problem of joint estimation of states and some constant parameters for a class of nonlinear discrete-time systems. This class contains systems that could be transformed into a quasi-LPV (linear parameter varying) polytopic model in the Takagi-Sugeno (T-S) form. Such systems could have unmeasured premise variables, a case usually overlooked in the observer design literature. We assert that, for such systems in discrete-time, the current literature lacks design strategies for joint state and parameter estimation. To this end, we adapt the existing literature on continuous-time linear systems for joint state and time-varying parameter estimation. We first develop the discrete-time version of this result for linear systems. A Lyapunov approach is used to illustrate stability, and bounds for the estimation error are obtained via the bounded real lemma. We use this result to achieve our objective for a design procedure for a class of nonlinear systems with constant parameters. This results in less conservative conditions and a simplified design procedure. A basic waste water treatment plant simulation example is discussed to illustrate the design procedure.

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45

Cao, Yong-Yan, und Anke Xue. „Comments on “Quasi-min–max MPC algorithm for LPV systems” by Y. Lu and Y. Arkun, Automatica 36 (2000) 527–540“. Automatica 40, Nr. 7 (Juli 2004): 1281–82. http://dx.doi.org/10.1016/j.automatica.2003.10.026.

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46

CAO, Y. „Comments on ?Quasi-min?max MPC algorithm for LPV systems? by Y. Lu and Y. Arkun, Automatica 36 (2000) 527$ndash;540“. Automatica 40, Nr. 7 (Juli 2004): 1281–82. http://dx.doi.org/10.1016/s0005-1098(04)00065-2.

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47

Su, Yang, Kok Kiong Tan und Tong Heng Lee. „Tube based Quasi-min-max Output Feedback MPC for LPV Systems1“. IFAC Proceedings Volumes 45, Nr. 15 (2012): 186–91. http://dx.doi.org/10.3182/20120710-4-sg-2026.00010.

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48

Lim, Jihoon, Patrick Kirchen und Ryozo Nagamune. „LPV Controller Design for Diesel Engine SCR Aftertreatment Systems based on Quasi-LPV Models“. IEEE Control Systems Letters, 2020, 1. http://dx.doi.org/10.1109/lcsys.2020.3046447.

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49

Fazli, Ali, und Mohammad Hosein Kazemi. „Robotic arm tracking control through smooth switching LPV controller based on LPV modeling and torque approximation“. Industrial Robot: the international journal of robotics research and application, 31.01.2024. http://dx.doi.org/10.1108/ir-07-2023-0142.

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Purpose This paper aims to propose a new linear parameter varying (LPV) controller for the robot tracking control problem. Using the identification of the robot dynamics in different work space points about modeling trajectory based on the least square of error algorithm, an LPV model for the robotic arm is extracted. Design/methodology/approach Parameter set mapping based on parameter component analysis results in a reduced polytopic LPV model that reduces the complexity of the implementation. An approximation of the required torque is computed based on the reduced LPV models. The state-feedback gain of each zone is computed by solving some linear matrix inequalities (LMIs) to sufficiently decrease the time derivative of a Lyapunov function. A novel smoothing method is used for the proposed controller to switch properly in the borders of the zones. Findings The polytopic set of the resulting gains creates the smooth switching polytopic LPV (SS-LPV) controller which is applied to the trajectory tracking problem of the six-degree-of-freedom PUMA 560 robotic arm. A sufficient condition ensures that the proposed controller stabilizes the polytopic LPV system against the torque estimation error. Practical implications Smoothing of the switching LPV controller is performed by defining some tolerances and creating some quasi-zones in the borders of the main zones leading to the compressed main zones. The proposed torque estimation is not a model-based technique; so the model variation and other disturbances cannot destroy the performance of the suggested controller. The proposed control scheme does not have any considerable computational load, because the control gains are obtained offline by solving some LMIs, and the torque computation is done online by a simple polytopic-based equation. Originality/value In this paper, a new SS-LPV controller is addressed for the trajectory tracking problem of robotic arms. Robot workspace is zoned into some main zones in such a way that the number of models in each zone is almost equal. Data obtained from the modeling trajectory is used to design the state-feedback control gain.

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Coutinho, Pedro H. S., Luciano G. Moreira und Reinaldo M. Palhares. „Event‐triggered control of quasi‐LPV systems with communication delays“. International Journal of Robust and Nonlinear Control, 27.07.2022. http://dx.doi.org/10.1002/rnc.6304.

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