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Journal articles on the topic 'Learning and control'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC

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2

Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 281–304. http://dx.doi.org/10.1146/annurev-control-053018-023744.

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Estimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoo

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3

Antsaklis, P. J. "Intelligent Learning Control." IEEE Control Systems 15, no. 3 (1995): 5–7. http://dx.doi.org/10.1109/mcs.1995.594467.

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4

Ali, S. Nageeb. "Learning Self-Control *." Quarterly Journal of Economics 126, no. 2 (2011): 857–93. http://dx.doi.org/10.1093/qje/qjr014.

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5

Barto, Andrew G. "Reinforcement learning control." Current Opinion in Neurobiology 4, no. 6 (1994): 888–93. http://dx.doi.org/10.1016/0959-4388(94)90138-4.

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6

Matsubara, Takamitsu. "Learning Control Policies by Reinforcement Learning." Journal of the Robotics Society of Japan 36, no. 9 (2018): 597–600. http://dx.doi.org/10.7210/jrsj.36.597.

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7

Dang, Ngoc Trung, and Phuong Nam Dao. "Data-Driven Reinforcement Learning Control for Quadrotor Systems." International Journal of Mechanical Engineering and Robotics Research 13, no. 5 (2024): 495–501. http://dx.doi.org/10.18178/ijmerr.13.5.495-501.

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This paper aims to solve the tracking problem and optimality effectiveness of an Unmanned Aerial Vehicle (UAV) by model-free data Reinforcement Learning (RL) algorithms in both sub-systems of attitude and position. First, a cascade UAV model structure is given to establish the control system diagram with two corresponding attitude and position control loops. Second, based on the computation of the time derivative of the Bellman function by two different methods, the combination of the Bellman function and the optimal control is adopted to maintain the control signal as time converges to infini

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8

Freeman, Chris, and Ying Tan. "Iterative learning control and repetitive control." International Journal of Control 84, no. 7 (2011): 1193–95. http://dx.doi.org/10.1080/00207179.2011.596574.

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9

Recht, Benjamin. "A Tour of Reinforcement Learning: The View from Continuous Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 253–79. http://dx.doi.org/10.1146/annurev-control-053018-023825.

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This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provi

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10

Ravichandar, Harish, Athanasios S. Polydoros, Sonia Chernova, and Aude Billard. "Recent Advances in Robot Learning from Demonstration." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 297–330. http://dx.doi.org/10.1146/annurev-control-100819-063206.

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In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an

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11

Hu, Bin, Kaiqing Zhang, Na Li, Mehran Mesbahi, Maryam Fazel, and Tamer Başar. "Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies." Annual Review of Control, Robotics, and Autonomous Systems 6, no. 1 (2023): 123–58. http://dx.doi.org/10.1146/annurev-control-042920-020021.

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Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical properties of these methods in the context of control and reinforcement learning. This article surveys some of the recent developments on policy optimization, a gradient-based iterative approach for feedback control synthesis that has been popularized by successes of reinforcement learning. We take an interdisciplinary perspective in our exposition that connects control theory, reinforcement learning, and large-scale

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12

WU, Q. H. "Reinforcement learning control using interconnected learning automata." International Journal of Control 62, no. 1 (1995): 1–16. http://dx.doi.org/10.1080/00207179508921531.

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13

You, Byungyong. "Normalized Learning Rule for Iterative Learning Control." International Journal of Control, Automation and Systems 16, no. 3 (2018): 1379–89. http://dx.doi.org/10.1007/s12555-017-0194-z.

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14

Zhang, Quanqi, Chengwei Wu, Haoyu Tian, Yabin Gao, Weiran Yao, and Ligang Wu. "Safety reinforcement learning control via transfer learning." Automatica 166 (August 2024): 111714. http://dx.doi.org/10.1016/j.automatica.2024.111714.

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15

Achille, Alessandro, and Stefano Soatto. "A Separation Principle for Control in the Age of Deep Learning." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 287–307. http://dx.doi.org/10.1146/annurev-control-060117-105140.

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We review the problem of defining and inferring a state for a control system based on complex, high-dimensional, highly uncertain measurement streams, such as videos. Such a state, or representation, should contain all and only the information needed for control and discount nuisance variability in the data. It should also have finite complexity, ideally modulated depending on available resources. This representation is what we want to store in memory in lieu of the data, as it separates the control task from the measurement process. For the trivial case with no dynamics, a representation can

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16

Youssef, Ayman, Mohamed El Telbany, and Abdelhalim Zekry. "Reinforcement Learning for Online Maximum Power Point Tracking Control." Journal of Clean Energy Technologies 4, no. 4 (2015): 245–48. http://dx.doi.org/10.7763/jocet.2016.v4.290.

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17

Amor, Heni Ben, Shuhei Ikemoto, Takashi Minato, and Hiroshi Ishiguro. "1P1-E07 Learning Android Control using Growing Neural Networks." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2006 (2006): _1P1—E07_1—_1P1—E07_4. http://dx.doi.org/10.1299/jsmermd.2006._1p1-e07_1.

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18

SUGANUMA, Yoshinori, and Masami ITO. "Learning Control and Knowledge." Transactions of the Society of Instrument and Control Engineers 22, no. 8 (1986): 841–48. http://dx.doi.org/10.9746/sicetr1965.22.841.

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19

Marino, R., S. Scalzi, P. Tomei, and C. M. Verrelli. "Generalized PID learning control." IFAC Proceedings Volumes 44, no. 1 (2011): 3629–34. http://dx.doi.org/10.3182/20110828-6-it-1002.01606.

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20

Buchli, Jonas, Freek Stulp, Evangelos Theodorou, and Stefan Schaal. "Learning variable impedance control." International Journal of Robotics Research 30, no. 7 (2011): 820–33. http://dx.doi.org/10.1177/0278364911402527.

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21

Dada, Maqbool, and Richard Marcellus. "Process Control with Learning." Operations Research 42, no. 2 (1994): 323–36. http://dx.doi.org/10.1287/opre.42.2.323.

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22

Huang, S. N., and S. Y. Lim. "Predictive Iterative Learning Control." Intelligent Automation & Soft Computing 9, no. 2 (2003): 103–12. http://dx.doi.org/10.1080/10798587.2000.10642847.

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23

Schaal, Stefan, and Christopher G. Atkeson. "Learning Control in Robotics." IEEE Robotics & Automation Magazine 17, no. 2 (2010): 20–29. http://dx.doi.org/10.1109/mra.2010.936957.

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24

Hart, Stephen, and Roderic Grupen. "Learning Generalizable Control Programs." IEEE Transactions on Autonomous Mental Development 3, no. 3 (2011): 216–31. http://dx.doi.org/10.1109/tamd.2010.2103311.

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25

Gallego, Jorge, Elise Nsegbe, and Estelle Durand. "Learning in Respiratory Control." Behavior Modification 25, no. 4 (2001): 495–512. http://dx.doi.org/10.1177/0145445501254002.

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26

Ekanayake, Jinendra, Aswin Chari, Claudia Craven, et al. "Learning to control ICP." Fluids and Barriers of the CNS 12, Suppl 1 (2015): P12. http://dx.doi.org/10.1186/2045-8118-12-s1-p12.

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27

Evans, Glen. "Getting learning under control." Australian Educational Researcher 15, no. 1 (1988): 1–17. http://dx.doi.org/10.1007/bf03219398.

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28

Wang, C., and D. J. Hill. "Learning From Neural Control." IEEE Transactions on Neural Networks 17, no. 1 (2006): 130–46. http://dx.doi.org/10.1109/tnn.2005.860843.

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29

Smith, Ian. "Tuberculosis Control Learning Games." Tropical Doctor 23, no. 3 (1993): 101–3. http://dx.doi.org/10.1177/004947559302300304.

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In teaching health workers about tuberculosis (TB) control we frequently concentrate on the technological aspects, such as diagnosis, treatment and recording. Health workers also need to understand the sociological aspects of TB control, particularly those that influence the likelihood of diagnosis and cure. Two games are presented that help health workers comprehend the reasons why TB patients often delay in presenting for diagnosis, and why they then frequently default from treatment.

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30

ARIMOTO, Suguru. "Theory of Learning Control." Journal of the Society of Mechanical Engineers 93, no. 856 (1990): 180–86. http://dx.doi.org/10.1299/jsmemag.93.856_180.

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31

Najafi, Esmaeil, Robert Babuska, and Gabriel A. D. Lopes. "Learning Sequential Composition Control." IEEE Transactions on Cybernetics 46, no. 11 (2016): 2559–69. http://dx.doi.org/10.1109/tcyb.2015.2481081.

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32

Shi, Jia, Furong Gao, and Tie-Jun Wu. "From Two-Dimensional Linear Quadratic Optimal Control to Iterative Learning Control. Paper 2. Iterative Learning Controls for Batch Processes." Industrial & Engineering Chemistry Research 45, no. 13 (2006): 4617–28. http://dx.doi.org/10.1021/ie051298a.

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33

Poot, Maurice, Jim Portegies, and Tom Oomen. "On the Role of Models in Learning Control: Actor-Critic Iterative Learning Control." IFAC-PapersOnLine 53, no. 2 (2020): 1450–55. http://dx.doi.org/10.1016/j.ifacol.2020.12.1918.

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34

Pasamontes, Manuel, José Domingo Alvarez, José Luis Guzman, and Manuel Berenguel. "Learning Switching Control: A Tank Level-Control Exercise." IEEE Transactions on Education 55, no. 2 (2012): 226–32. http://dx.doi.org/10.1109/te.2011.2162239.

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35

Wawrzyński, Paweł. "Control Policy with Autocorrelated Noise in Reinforcement Learning for Robotics." International Journal of Machine Learning and Computing 5, no. 2 (2015): 91–95. http://dx.doi.org/10.7763/ijmlc.2015.v5.489.

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36

Genders, Wade, and Saiedeh Razavi. "Evaluating Reinforcement Learning State Representations for Adaptive Traffic Signal Control." International Journal of Traffic and Transportation Management 1, no. 1 (2019): 19–26. http://dx.doi.org/10.5383/jttm.01.01.003.

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37

AZLAN, Norsinnira, and Hiroshi YAMAURA. "20204 Study of Feedback Error Learning Control for Underactuated Systems." Proceedings of Conference of Kanto Branch 2009.15 (2009): 149–50. http://dx.doi.org/10.1299/jsmekanto.2009.15.149.

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38

Irfan, C. M. Althaff, Karim Ouzzane, Shusaku Nomura, and Yoshimi Fukumura. "211 AN ACCESS CONTROL SYSTEM For E-Learning MANAGEMENT SYSTEMS." Proceedings of Conference of Hokuriku-Shinetsu Branch 2010.47 (2010): 59–60. http://dx.doi.org/10.1299/jsmehs.2010.47.59.

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39

白家納, 白家納, та 黃崇能 Pachara Opattrakarnkul. "以深度學習模式估測控制之駕駛輔助系統的研發". 理工研究國際期刊 12, № 1 (2022): 015–24. http://dx.doi.org/10.53106/222344892022041201002.

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<p>Adaptive cruise control (ACC) systems are designed to provide longitudinal assistance to enhance safety and driving comfort by adjusting vehicle velocity to maintain a safe distance between the host vehicle and the preceding vehicle. Generally, using model predictive control (MPC) in ACC systems provides high responsiveness and lower discomfort by solving real-time constrained optimization problems but results in computational load. This paper presents an architecture of deep learning based on model predictive control in ACC systems to avoid real-time optimization problems required by

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40

Hein, Daniel, Steffen Limmer, and Thomas A. Runkler. "Interpretable Control by Reinforcement Learning." IFAC-PapersOnLine 53, no. 2 (2020): 8082–89. http://dx.doi.org/10.1016/j.ifacol.2020.12.2277.

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41

Kurata, K. "GREENHOUSE CONTROL BY MACHINE LEARNING." Acta Horticulturae, no. 230 (September 1988): 195–200. http://dx.doi.org/10.17660/actahortic.1988.230.23.

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42

Žáková, Katarína, and Richard Balogh. "Control Engineering Home Learning Kit." IFAC-PapersOnLine 55, no. 4 (2022): 310–15. http://dx.doi.org/10.1016/j.ifacol.2022.06.051.

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43

Yuan, Xu, Lucian Buşoniu, and Robert Babuška. "Reinforcement Learning for Elevator Control." IFAC Proceedings Volumes 41, no. 2 (2008): 2212–17. http://dx.doi.org/10.3182/20080706-5-kr-1001.00373.

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44

De Loo, I. "Management control bij action learning." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. 10 (2003): 445–52. http://dx.doi.org/10.5117/mab.77.11785.

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In tegenstelling tot de jaren zestig en zeventig van de vorige eeuw komt uit evaluaties van action learningprogramma’s steeds vaker naar voren dat zij weliswaar hebben geleid tot persoonlijke groei, maar niet langer tot organisatiegroei. In dit artikel wordt betoogd dat één van de belangrijkste oorzaken daarvan is dat er geen speci&#64257;eke rol voor management controlsystemen is weggelegd bij action learning. Wanneer echter vormen van ‘trial and error’ en ‘intuitive control’ zouden worden toegepast, is het niet ondenkbeeldig dat organisatiegroei wél weer gerealiseerd wordt, mits action l

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45

Benavent, Christophe. "CRM, LEARNING AND ORGANIZATIONAL CONTROL." JISTEM Journal of Information Systems and Technology Management 3, no. 2 (2006): 193–210. http://dx.doi.org/10.4301/s1807-17752006000200006.

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46

Layne, Jeffery R., and Kevin M. Passino. "Fuzzy Model Reference Learning Control." Journal of Intelligent and Fuzzy Systems 4, no. 1 (1996): 33–47. http://dx.doi.org/10.3233/ifs-1996-4103.

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47

Horowitz, Roberto. "Learning Control Applications to Mechatronics." JSME international journal. Ser. C, Dynamics, control, robotics, design and manufacturing 37, no. 3 (1994): 421–30. http://dx.doi.org/10.1299/jsmec1993.37.421.

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48

Jiménez, E., and M. Rodríguez. "WEB BASED PROCESS CONTROL LEARNING." IFAC Proceedings Volumes 39, no. 6 (2006): 349–54. http://dx.doi.org/10.3182/20060621-3-es-2905.00061.

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49

Holder, Tim. "Motor Control, Learning and Development." Journal of Sports Sciences 26, no. 12 (2008): 1375–76. http://dx.doi.org/10.1080/02640410802271547.

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50

Suganuma, Y., and M. Ito. "Learning in movement and control." IEEE Transactions on Systems, Man, and Cybernetics 19, no. 2 (1989): 258–70. http://dx.doi.org/10.1109/21.31031.

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