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Готові списки джерел за темами / Learning based control / Статті в журналах

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Статті в журналах з теми "Learning based control"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 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

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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3

Berti, Dyah Permatasari, Gunarhadi, and Riyadi. "The influence of problem based learning towards social science learning outcomes viewed from learning interest." International Journal of Evaluation and Research in Education (IJERE) 8, no. 1 (2019): 39–46. https://doi.org/10.11591/ijere.v8i1.15594.

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The aim of this study is to determine the influences of Problem Based Learning and learning interest at improving the cognitive learning outcomes in social science of fourth-grade elementary school students. This study is a quasi-experiment study with pre-test and post-test control group design. The sample collection is taken with stratified cluster random sampling technique. The sample of this study is 109 fourth-grade elementary school students, consisted of 50 students from experiment group and 59 students from control group. The students from experiment group are given the application of P

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4

Hadi Saputro, Suyatno. "The Impact of Problem Based Learning on Learning Outcomes in Nursing Students." International Journal of Multidisciplinary Research and Analysis 05, no. 10 (2022): 2784–88. http://dx.doi.org/10.47191/ijmra/v5-i10-29.

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This study aims to determine the impact of problem-based learning on nursing student learning outcomes. The research method used is quasi-experimental by comparing the two groups, namely the group treated with problem-based learning and conventional learning. The research subjects were 7th semester nursing students, totaling 68 students who were divided into two groups, experiment with problem-based learning and control with conventional learning. Analysis of the data in this study by comparing learning outcomes in the two groups, experimental and control. The results of data analysis showed t

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5

Hermawan, Edy, I. Putu Eka Indrawan, and Ni Luh Lianjayani. "Pengaruh Model Pembelajaran Problem Based Learning Berbantuan E-Learning terhadap Kreativitas dan Hasil Belajar Matematika." Emasains 7, no. 2 (2018): 170–77. https://doi.org/10.5281/zenodo.2548093.

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This study aimed to assess the effect of Problem Based Learning model with e-learning to creativity and learning result of mathematics on tenth grade student of SMK Negeri 4 Denpasar on academic year 2017/2018. This type of research was quasi experiment, using non equivalent posttest-only control group design. The population in this study were the student of tenth grade PH of SMK Negeri 4 Denpasar with 4 classes (144 students). Using multistage random sampling technique, 2 classes were obtained as sample, wich is tenth grade PH 3 (36 students) as control group and tenth grade PH 1 (36 students

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6

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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7

Suyatno, Hadi Saputro, Mustaji, and Arianto Fajar. "The Impact of Problem Based Learning on Learning Outcomes in Nursing Students." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 05, no. 10 (2022): 2784–88. https://doi.org/10.5281/zenodo.7214984.

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Анотація:

This study aims to determine the impact of problem-based learning on nursing student learning outcomes. The research method used is quasi-experimental by comparing the two groups, namely the group treated with problem-based learning and conventional learning. The research subjects were 7th semester nursing students, totaling 68 students who were divided into two groups, experiment with problem-based learning and control with conventional learning. Analysis of the data in this study by comparing learning outcomes in the two groups, experimental and control. The results of data analysis showed t

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8

Songjun, M., and D. H. Owens. "POLYNOMIALS BASED ITERATIVE LEARNING CONTROL ALGORITHM." IFAC Proceedings Volumes 40, no. 18 (2007): 623–29. http://dx.doi.org/10.3182/20070927-4-ro-3905.00103.

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9

Mirwald, Jonas, Johannes Ultsch, Ricardo de Castro, and Jonathan Brembeck. "Learning-Based Cooperative Adaptive Cruise Control." Actuators 10, no. 11 (2021): 286. http://dx.doi.org/10.3390/act10110286.

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Traffic congestion and the occurrence of traffic accidents are problems that can be mitigated by applying cooperative adaptive cruise control (CACC). In this work, we used deep reinforcement learning for CACC and assessed its potential to outperform model-based methods. The trade-off between distance-error minimization and energy consumption minimization whilst still ensuring operational safety was investigated. Alongside a string stability condition, robustness against burst errors in communication also was incorporated, and the effect of preview information was assessed. The controllers were

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10

Xiong, Xiaofeng, Cao Danh Do, and Poramate Manoonpong. "Learning-Based Multifunctional Elbow Exoskeleton Control." IEEE Transactions on Industrial Electronics 69, no. 9 (2022): 9216–24. http://dx.doi.org/10.1109/tie.2021.3116572.

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11

Ghosh, J., and B. Paden. "A pseudoinverse-based iterative learning control." IEEE Transactions on Automatic Control 47, no. 5 (2002): 831–37. http://dx.doi.org/10.1109/tac.2002.1000282.

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12

Milentijevic, Ivan, Vladimir Ciric, and Oliver Vojinovic. "Version control in project-based learning." Computers & Education 50, no. 4 (2008): 1331–38. http://dx.doi.org/10.1016/j.compedu.2006.12.010.

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13

Nygren, Johannes, Kristiaan Pelckmans, and Bengt Carlsson. "Approximate adjoint-based iterative learning control." International Journal of Control 87, no. 5 (2013): 1028–46. http://dx.doi.org/10.1080/00207179.2013.865144.

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14

Mishra, Sandipan, Ufuk Topcu, and Masayoshi Tomizuka. "Optimization-Based Constrained Iterative Learning Control." IEEE Transactions on Control Systems Technology 19, no. 6 (2011): 1613–21. http://dx.doi.org/10.1109/tcst.2010.2083663.

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15

Çimen, Gökçen, Zümra Kavafoǧlu, Ersan Kavafoǧlu, Tolga Çapın, and Haşmet Gürcay. "Skill learning based catching motion control." Computer Animation and Virtual Worlds 26, no. 3-4 (2015): 217–25. http://dx.doi.org/10.1002/cav.1659.

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16

Ye, Yongqiang, Danwei Wang, Bin Zhang, and Yigang Wang. "Simple LMI based learning control design." Asian Journal of Control 11, no. 1 (2009): 74–77. http://dx.doi.org/10.1002/asjc.82.

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17

Gao, Zhenhai, Jun Wang, Hongyu Hu, and Dazhi Wang. "Vehicle Longitudinal Control Algorithm Based on Iterative Learning Control." SAE International Journal of Passenger Cars - Mechanical Systems 9, no. 1 (2016): 366–72. http://dx.doi.org/10.4271/2016-01-1653.

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18

Moore, Kevin L., Mohua Ghosh, and Yang Quan Chen. "Spatial-based iterative learning control for motion control applications." Meccanica 42, no. 2 (2007): 167–75. http://dx.doi.org/10.1007/s11012-006-9035-5.

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19

Luzeaux, D. "Process control and machine learning: rule-based incremental control." IEEE Transactions on Automatic Control 39, no. 6 (1994): 1166–71. http://dx.doi.org/10.1109/9.293176.

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20

Sakaino, Sho. "Prediction and Control by Bilateral Control-Based Imitation Learning." Journal of the Robotics Society of Japan 40, no. 9 (2022): 778–83. http://dx.doi.org/10.7210/jrsj.40.778.

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21

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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22

Ahamed, VM Niaz, Arul Prakash, and Mohamed Ziyath. "TCC-HDL: A Hybrid Deep Learning Based Traffic Congestion Control System for VANET." Indian Journal Of Science And Technology 16, no. 32 (2023): 2548–59. http://dx.doi.org/10.17485/ijst/v16i32.1319.

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23

Basha, MD Chan. "AI Based Traffic Control System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48317.

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This project explores the development of an AI-based traffic control system aimed at enhancing urban traffic management. By utilizing machine learning algorithms and real-time data from various sources, including traffic cameras, sensors, and GPS devices, the system analyzes traffic patterns and predicts congestion. We are using several AI and ML models, including Reinforcement Learning, Deep Learning, Neural Network and also Computer Vision. The integration of vehicle to infrastructure communication further enhances responsiveness, allowing for timely updates to drivers. Simulation results in

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24

Deepti, Pandey. "EFFECT OF 'JIGSAW' COOPERATIVE LEARNING STRATEGY ON QUALITY OF LEARNING OUTCOMES AMONG SECONDARY SCHOOL STUDENTS." Scholarly Research Journal for Interdisciplinary Studies 11, no. 77 (2023): 235–45. https://doi.org/10.5281/zenodo.8174125.

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<em>The present study was undertaken to investigate the effect of teaching through jigsaw technique on the learning outcomes in science for Xth class. The data were obtained through pre and post tests from 120 (60 experimental and 60 control group) tenth class randomly selected students from Kendriya Vidyalaya, Amritsar district of Punjab (India). Experimental group was taught through jigsaw technique and the control group was taught through traditional teaching techniques. The result of the study revealed significant difference between the learning outcomes in Academic Achievement gain scores

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25

Fadilah, Ifa Fitriyatul, and Albrian Fiky Prakoso. "PERBANDINGAN PROBLEM BASED CONVENTIONAL LEARNING DENGAN CREATIVE PROBLEM BASED LEARNING TERHADAP SELF REGULATED LEARNING." Edunomic Jurnal Pendidikan Ekonomi 12, no. 2 (2024): 169–82. https://doi.org/10.33603/ejpe.v12i2.9007.

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This study aims to compare Problem Based Conventional Learning (PBCL) and Creative Problem Based Learning (CPBL) learning models on self-regulated learning in economics. PBCL is a conventional teaching model combined with problem-based learning, which emphasises problem solving with a conventional approach while CPBL is a learning model in which there is an element of creativity that emphasises the use of creativity in solving problems. This research is a type of Quasi Experimental research with Non Equivalent Control Group design. The subjects of this study were students of class XI 5 as the

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26

Ibrahim, Ibrahim Abdellatif, and Wafaa Fathi Sleem. "Team Based Learning." International Journal for Innovation Education and Research 6, no. 1 (2018): 159–74. http://dx.doi.org/10.31686/ijier.vol6.iss1.940.

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Background:Team based learning is a strategy that combines features of student-centeredness and problem solving attributes. It also enhance students’ engagement which achieve desired outcomes such as retention; academic achievement and improved critical thinking. Aim: The present study aimed to investigate the effect of implementing team based learning on students' engagement in nursing administration course. Subjects and Methods: A quasi experimental design was used. The study sample included 251 students who were studied nursing administration course during the first term of academic y

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27

Henrique, Bruna Couto Molinar, Leonardo Couto Molinar Henrique, and Humberto Molinar Henrique. "Arduino based platform for process control learning." Journal of Engineering and Exact Sciences 6, no. 5 (2020): 0585–93. http://dx.doi.org/10.18540/jcecvl6iss5pp0585-0593.

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This work deals with implementation of an experimental flowrate control unit using free and low-cost hardware and software. The open-source software Processing was used to develop the source codes and user graphical interface and the open-source electronic prototyping platform Arduino was used to acquire data from an experimental unit. Work presents descriptions of the experimental setup, the real-time PID controllers used and theoretical/conceptual issues of Arduino. PID controllers based on internal model control, minimization of the integral of time-weighted absolute error, Ziegler-Nichols,

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28

Diveev, Askhat, and Elizaveta Shmalko. "Synthesized optimal control based on machine learning." Journal of Physics: Conference Series 1727 (January 2021): 012006. http://dx.doi.org/10.1088/1742-6596/1727/1/012006.

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29

Butcher, Mark, Alireza Karimi, and Roland Longchamp. "Iterative Learning Control based on Stochastic Approximation." IFAC Proceedings Volumes 41, no. 2 (2008): 1478–83. http://dx.doi.org/10.3182/20080706-5-kr-1001.00253.

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30

Liu, Yang, Zhengxing Chen, Kittipat Virochsiri, Juan Wang, Jiahao Wu, and Feng Liang. "Reinforcement Learning-based Product Delivery Frequency Control." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15355–61. http://dx.doi.org/10.1609/aaai.v35i17.17803.

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Frequency control is an important problem in modern recommender systems. It dictates the delivery frequency of recommendations to maintain product quality and efficiency. For example, the frequency of delivering promotional notifications impacts daily metrics as well as the infrastructure resource consumption (e.g. CPU and memory usage). There remain open questions on what objective we should optimize to represent business values in the long term best, and how we should balance between daily metrics and resource consumption in a dynamically fluctuating environment. We propose a personalized me

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31

Dixon, W. E., E. Zergeroglu, D. M. Dawson, and B. T. Costic. "Repetitive learning control: a Lyapunov-based approach." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 32, no. 4 (2002): 538–45. http://dx.doi.org/10.1109/tsmcb.2002.1018772.

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32

Emelianova, Julia Pavlovna. "Output Based Iterative Learning Control with Saturation." Differential Equations and Control Processes, no. 3 (2024): 47–66. https://doi.org/10.21638/11701/spbu35.2024.304.

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The paper considers a linear discrete-time system operating in a repetitive mode to track a reference trajectory with a given accuracy. The system parameters are incompletely known and are described by the affine uncertainty model. A new iterative learning control design method based on information about the measured output signal is obtained; this method takes into account the saturation-type nonlinearity inherent in the actuators of robots and allows achieving the required accuracy. The problem statement is motivated by the development trends of high-precision smart and additive manufacturin

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33

Lin, C. S., and H. Kim. "CMAC-based adaptive critic self-learning control." IEEE Transactions on Neural Networks 2, no. 5 (1991): 530–33. http://dx.doi.org/10.1109/72.134290.

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34

ZHENG, Yichen, Yi ZHANG, and Jianming HU. "Iterative Learning Based Adaptive Traffic Signal Control." Journal of Transportation Systems Engineering and Information Technology 10, no. 6 (2010): 34–40. http://dx.doi.org/10.1016/s1570-6672(09)60070-2.

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35

Axelsson, Patrik, Rickard Karlsson, and Mikael Norrlöf. "Estimation-based norm-optimal iterative learning control." Systems & Control Letters 73 (November 2014): 76–80. http://dx.doi.org/10.1016/j.sysconle.2014.08.007.

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36

Khong, Sei Zhen, Dragan Nešić, and Miroslav Krstić. "Iterative learning control based on extremum seeking." Automatica 66 (April 2016): 238–45. http://dx.doi.org/10.1016/j.automatica.2015.12.019.

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37

Padmanabhan, Regina, Nader Meskin, and Wassim M. Haddad. "LEARNING-BASED CONTROL OF CANCER CHEMOTHERAPY TREATMENT." IFAC-PapersOnLine 50, no. 1 (2017): 15127–32. http://dx.doi.org/10.1016/j.ifacol.2017.08.2247.

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38

Chi, Ronghu, Yangchun Wei, Wenlong Yao, and Jianmin Xing. "Observer-based data-driven iterative learning control." International Journal of Systems Science 51, no. 13 (2020): 2343–59. http://dx.doi.org/10.1080/00207721.2020.1793427.

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39

Sage, Kingsley, A. Jonathan Howell, Hilary Buxton, and Antonis Argyros. "Learning temporal structure for task based control." Image and Vision Computing 26, no. 1 (2008): 39–52. http://dx.doi.org/10.1016/j.imavis.2005.08.010.

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40

Lee, Donghwan, and Deok-Jin Lee. "Guest Editorial: Reinforcement Learning based Control Applications." Journal of Institute of Control, Robotics and Systems 28, no. 11 (2022): 963. http://dx.doi.org/10.5302/j.icros.2022.22.9005.

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41

MASUYA, Nozomu, Tetsuro AKAGAWA, Koki YAMANE, Takuya KUSUME, and Sho SAKAINO. "Constraint-Aware Bilateral Control-Based Imitation Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2023 (2023): 2A1—D15. http://dx.doi.org/10.1299/jsmermd.2023.2a1-d15.

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42

Tambe, Prof S. L., Sarthak Gaikwad, Pravin Trimbake, Ranjit Mali, and Shirish Kamble. "Density Based Traffic Control Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1166–69. http://dx.doi.org/10.22214/ijraset.2023.56556.

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Abstract: “Density Based Traffic Control Using Machine Learning” is a project that is designed to develop a density based dynamic traffic signal system. The signal timing changes automatically on sensing the traffic density at the junction. Traffic congestion is a severe problem in many major cities across the world. Conventional traffic light system is based on the fixed time concept allotted to each side of the junction which cannot be varied as per varying traffic density

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43

Huang, C., S. Seidel, and J. Bräunig. "Cascaded reinforcement learning based supply temperature control." Journal of Physics: Conference Series 2600, no. 7 (2023): 072010. http://dx.doi.org/10.1088/1742-6596/2600/7/072010.

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Abstract In this work, a Q-learning based supply temperature control approach for a demonstrator building is proposed. The purpose is to improve the temperature behaviour inside the building and to tackle comfort problems such as overheating and undercooling which cannot be coped with by the standard heating curve. The Q-learning controller considers predicted future weather data as system states. This can be shown to be superior to Q-learning controllers without weather prediction. Furthermore, in order for the controller to capture different thermal effects of different time constants, a cas

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44

Ahn, Heejin. "Guest Editorial: Learning-based Model Predictive Control." Journal of Institute of Control, Robotics and Systems 31, no. 2 (2025): 77. https://doi.org/10.5302/j.icros.2025.25.9001.

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45

Xu, Zeyuan, Wei Xing Zheng, Yujia Wang, Danwei Wang, and Zhe Wu. "Federated learning-based distributed model predictive control." Journal of Process Control 152 (August 2025): 103472. https://doi.org/10.1016/j.jprocont.2025.103472.

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46

Dewi, Anjani, Maridi, and Suciati. "Inquiry Based Learning Module to Empower Cooperation Skills." Journal of Education and Learning (EduLearn) 12, no. 2 (2018): 172–78. https://doi.org/10.11591/edulearn.v12i2.8268.

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The education in the 21 st  century focuses on knowledge and encourages  students to generate information and encourage developing new skills. The framework of 21 st  century learning skill is communication, collaboration,  critical thinking, creativity and innovation. A cooperation skill in the world of education is an important thing to be done in learning. Therefore, a solution is needed to optimize student empower cooperation skills process by using the teaching material in the form of module. The objective of this research is to know the effectiveness of inquiry based

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47

Strenge, Lia, Xiaohan Jing, Ruth Boersma, et al. "Iterative learning control in prosumer-based microgrids with hierarchical control." IFAC-PapersOnLine 53, no. 2 (2020): 12251–58. http://dx.doi.org/10.1016/j.ifacol.2020.12.1145.

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48

Liu, Tingrui, Yan Ding, Pan Wang, Kang Zhao, and Jiahao Jia. "Stability Control of Transport Robot Based on Iterative Learning Control." Journal of Physics: Conference Series 2173, no. 1 (2022): 012061. http://dx.doi.org/10.1088/1742-6596/2173/1/012061.

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Abstract In this study, stability control for transport process of transport robot subjected to 2R manipulator movement, is investigated based on iterative learning control (ILC). The joint positions, speeds and accelerations are used as variables to establish the expression of driving torques of manipulator joints. According to the experience, the linear interference torque in the process of motion is determined. Three ILC algorithms are applied to achieve stability control, and good trajectory tracking results are obtained. Position tracking, speed tracking, and the maximum position error in

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Fajarwati, Aprilia, Basori Basori, and Puspanda Hatta. "Utilization of Open Video Repository-Assisted Inquiry Based Learning (IBL) on Computer Network Basics Course : Its Effect on Student Interest and Learning Outcomes." Journal of Global Hospitality and Tourism Technology 1, no. 1 (2024): 38–49. https://doi.org/10.5281/zenodo.12590381.

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Learning models that are not suitable in learning can make students' interests and learning outcomes less than optimal. Therefore, educators must make the selection of the right learning model. This study aims to investigate how Inquiry-Based Learning supported by an open video repository impacts student interest and learning outcomes. The research design used was a pseudo-experimental method with a pretest posttest control group design. The class that was given the treatment of using the Inquiry Based Learning learning model assisted by the Open Video Repository was an experimental class, whi

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Ozkan, Mehmet F., Abishek J. Rocque, and Yao Ma. "Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning." IFAC-PapersOnLine 54, no. 20 (2021): 882–88. http://dx.doi.org/10.1016/j.ifacol.2021.11.283.

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