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Journal articles on the topic 'Nonlinear decentralised control'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Udwadia, Firdaus E., Prasanth B. Koganti, Thanapat Wanichanon, and Dušan M. Stipanović. "Decentralised control of nonlinear dynamical systems." International Journal of Control 87, no. 4 (December 18, 2013): 827–43. http://dx.doi.org/10.1080/00207179.2013.861079.

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2

Lu, Q., S. Mei, W. Hu, Y. H. Song, M. Goto, and H. Konishi. "Decentralised nonlinear H∞ excitation control based on regulation linearisation." IEE Proceedings - Generation, Transmission and Distribution 147, no. 4 (2000): 245. http://dx.doi.org/10.1049/ip-gtd:20000441.

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3

Yan, Xing-Gang, Sarah K. Spurgeon, and Christopher Edwards. "DECENTRALISED SLIDING MODE CONTROL FOR NONMINIMUM PHASE NONLINEAR INTERCONNECTED SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 693–98. http://dx.doi.org/10.3182/20050703-6-cz-1902.00771.

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4

Li, G. J., T. T. Lie, C. B. Soh, and G. H. Yang. "Decentralised nonlinear H∞ control for stability enhancement in power systems." IEE Proceedings - Generation, Transmission and Distribution 146, no. 1 (1999): 19. http://dx.doi.org/10.1049/ip-gtd:19990042.

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5

Ji, Nan, Xing-Gang Yan, Zehui Mao, Dongya Zhao, and Bin Jiang. "Decentralised Sliding Mode Control for Nonlinear Interconnected Systems with Unknown Interconnections." IFAC-PapersOnLine 53, no. 2 (2020): 4064–69. http://dx.doi.org/10.1016/j.ifacol.2020.12.2374.

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6

Koo, Geun Bum, Jin Bae Park, and Young Hoon Joo. "Decentralised sampled-data control for large-scale systems with nonlinear interconnections." International Journal of Control 89, no. 10 (March 2, 2016): 1951–61. http://dx.doi.org/10.1080/00207179.2016.1145741.

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7

Benchabane, Achour, and Noureddine Bali. "Decentralised nonlinear predictive control for twin rotor multi-input multi-output system." International Journal of Modelling, Identification and Control 39, no. 3 (2021): 257. http://dx.doi.org/10.1504/ijmic.2021.123489.

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8

Ji, Nan, Xing-Gang Yan, Zehui Mao, Dongya Zhao, and Bin Jiang. "Decentralised state feedback stabilisation for nonlinear interconnected systems using sliding mode control*." International Journal of Systems Science 53, no. 5 (October 25, 2021): 1017–30. http://dx.doi.org/10.1080/00207721.2021.1986599.

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9

Bali, Noureddine, and Achour Benchabane. "Decentralised nonlinear predictive control for twin rotor multi-input multi-output system." International Journal of Modelling, Identification and Control 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijmic.2021.10045637.

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10

Becerril-Arreola, R., and A. G. Aghdam. "Decentralised nonlinear control with disturbance rejection for on-ramp metering in highways." IET Control Theory & Applications 1, no. 1 (January 1, 2007): 253–62. http://dx.doi.org/10.1049/iet-cta:20045192.

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11

Hua, Changchun, Guopin Liu, Zhenhua Bai, and Xinping Guan. "Decentralised adaptive control for a class of stochastic switched interconnected nonlinear systems." International Journal of Systems Science 47, no. 16 (December 15, 2015): 3782–91. http://dx.doi.org/10.1080/00207721.2015.1122848.

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12

Xi, Z., and Z. Ding. "Global decentralised output regulation for a class of large-scale nonlinear systems with nonlinear exosystem." IET Control Theory & Applications 1, no. 5 (September 1, 2007): 1504–11. http://dx.doi.org/10.1049/iet-cta:20060432.

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13

Yan, Xing-Gang, and Sarah K. Spurgeon. "Delay-independent decentralised output feedback control for large-scale systems with nonlinear interconnections." International Journal of Control 87, no. 3 (January 16, 2014): 473–82. http://dx.doi.org/10.1080/00207179.2013.841342.

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14

Liu, Ye, Yan Lin, and Ran Huang. "Decentralised adaptive output feedback control for interconnected nonlinear systems preceded by unknown hysteresis." International Journal of Control 88, no. 9 (March 5, 2015): 1712–25. http://dx.doi.org/10.1080/00207179.2015.1013990.

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15

Mu, Jianqiu, Xing-Gang Yan, Qingling Zhang, and Zehui Mao. "Decentralised Sliding Mode Control for Nonlinear Interconnected Systems in the Generalised Regular Form." IFAC-PapersOnLine 50, no. 1 (July 2017): 8850–55. http://dx.doi.org/10.1016/j.ifacol.2017.08.1541.

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16

Chang, Le, Chenghui Zhang, Xianfu Zhang, and Xiandong Chen. "Decentralised regulation of nonlinear multi-agent systems with directed network topologies." International Journal of Control 90, no. 11 (December 2, 2016): 2338–48. http://dx.doi.org/10.1080/00207179.2016.1245868.

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17

Zhang, Liuliu, Changchun Hua, Hongnian Yu, and Xinping Guan. "Decentralised adaptive control of switched interconnected high-order nonlinear systems with unknown control direction and time-delay." International Journal of Control 92, no. 4 (September 6, 2017): 875–85. http://dx.doi.org/10.1080/00207179.2017.1372634.

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18

Majidabad, Sajjad Shoja, Heydar Toosian Shandiz, and Amin Hajizadeh. "Decentralised sliding mode control of RL-derivative based fractional-order large-scale nonlinear systems." International Journal of Systems, Control and Communications 6, no. 3 (2015): 181. http://dx.doi.org/10.1504/ijscc.2015.068898.

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19

Ma, Yuechao, Shujie Jin, and Nannan Gu. "Decentralised memory static output feedback control for the nonlinear time-delay similar interconnected systems." International Journal of Systems Science 47, no. 10 (January 13, 2015): 2487–98. http://dx.doi.org/10.1080/00207721.2014.999264.

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20

Yu, Xiaowei, Yan Lin, and Xu Zhang. "Decentralised output feedback for a class of nonlinear systems via quantised sampled-data control." International Journal of Systems Science 48, no. 5 (September 9, 2016): 1002–8. http://dx.doi.org/10.1080/00207721.2016.1229078.

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21

Zhai, Jun-yong, Wen-ting Zha, and Shu-min Fei. "Semi-global decentralised output feedback stabilisation for a class of uncertain nonlinear systems." International Journal of Control 86, no. 6 (June 2013): 1149–57. http://dx.doi.org/10.1080/00207179.2013.784810.

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22

Lan, Qixun, Shihong Ding, Shihua Li, and Chuanlin Zhang. "Global decentralised stabilisation for a class of uncertain large-scale feedforward nonlinear systems." International Journal of Control 87, no. 6 (February 5, 2014): 1282–96. http://dx.doi.org/10.1080/00207179.2013.875223.

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23

Luo, Qiliang, Shan Xue, and Derong Liu. "Adaptive critic designs for decentralised robust control of nonlinear interconnected systems via event-triggering mechanism." International Journal of Systems Science 53, no. 5 (November 22, 2021): 1031–47. http://dx.doi.org/10.1080/00207721.2021.1987578.

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24

Wu, J., A. Yokoyama, Q. Lu, M. Goto, and H. Konishi. "Decentralised nonlinear equilibrium point adaptive control of generator for improving multimachine power system transient stability." IEE Proceedings - Generation, Transmission and Distribution 150, no. 6 (2003): 697. http://dx.doi.org/10.1049/ip-gtd:20030878.

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25

Huang, Yi-Shao, Xiaoxin Chen, Shao-Wu Zhou, Ling-Li Yu, and Zheng-Wu Wang. "HGO-based decentralised indirect adaptive fuzzy control for a class of large-scale nonlinear systems." International Journal of Systems Science 43, no. 6 (June 2012): 1133–45. http://dx.doi.org/10.1080/00207721.2010.545488.

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26

Liu, Wen-Jeng. "Design of decentralised variable structure observer for mismatched nonlinear uncertain large-scale systems." International Journal of Systems Science 42, no. 3 (March 2011): 349–57. http://dx.doi.org/10.1080/00207720903513343.

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27

Latrach, Chedia, Mourad Kchaou, and Hervé Guéguen. "H∞observer-based decentralised fuzzy control design for nonlinear interconnected systems: an application to vehicle dynamics." International Journal of Systems Science 48, no. 7 (December 15, 2016): 1485–95. http://dx.doi.org/10.1080/00207721.2016.1266527.

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28

Zhang, Xiuyu, Yan Lin, and Jianguo Wang. "High-gain observer based decentralised output feedback control for interconnected nonlinear systems with unknown hysteresis input." International Journal of Control 86, no. 6 (June 2013): 1046–59. http://dx.doi.org/10.1080/00207179.2013.773086.

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29

Gao, Shigen, Hairong Dong, Shihang Lyu, and Bin Ning. "Truncated adaptation design for decentralised neural dynamic surface control of interconnected nonlinear systems under input saturation." International Journal of Control 89, no. 7 (January 25, 2016): 1447–66. http://dx.doi.org/10.1080/00207179.2015.1135507.

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30

Han, Yu-Qun, and Hong-Sen Yan. "Observer-based multi-dimensional Taylor network decentralised adaptive tracking control of large-scale stochastic nonlinear systems." International Journal of Control 93, no. 7 (September 20, 2018): 1605–18. http://dx.doi.org/10.1080/00207179.2018.1521994.

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31

Yan, Xing-Gang, Christopher Edwards, and Sarah K. Spurgeon. "Decentralised robust sliding mode control for a class of nonlinear interconnected systems by static output feedback." Automatica 40, no. 4 (April 2004): 613–20. http://dx.doi.org/10.1016/j.automatica.2003.10.025.

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32

Xu, Yinyin, Shaocheng Tong, and Yongming Li. "Adaptive fuzzy decentralised fault-tolerant control for nonlinear large-scale systems with actuator failures and unmodelled dynamics." International Journal of Systems Science 46, no. 12 (November 14, 2013): 2195–209. http://dx.doi.org/10.1080/00207721.2013.859328.

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33

Liu, Liang, and Yifan Zhang. "Decentralised output-feedback control for a class of large-scale stochastic high-order upper-triangular nonlinear systems." International Journal of Systems Science 48, no. 4 (August 9, 2016): 838–48. http://dx.doi.org/10.1080/00207721.2016.1216202.

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34

Zhao, Bo, Derong Liu, Xiong Yang, and Yuanchun Li. "Observer-critic structure-based adaptive dynamic programming for decentralised tracking control of unknown large-scale nonlinear systems." International Journal of Systems Science 48, no. 9 (March 7, 2017): 1978–89. http://dx.doi.org/10.1080/00207721.2017.1296982.

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35

Tlili, Ali Sghaier, Sadok Dhbaibi, and Naceur Benhadj Braiek. "Robust decentralised observer based guaranteed cost control for nonlinear uncertain interconnected systems. Application to multi-machine power systems." International Journal of Systems Science 43, no. 9 (September 2012): 1713–27. http://dx.doi.org/10.1080/00207721.2010.549615.

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36

Wang, Zheng-Wu, Xueyun Chen, and Yi-Shao Huang. "Decentralised direct adaptive fuzzy control for a class of large-scale nonaffine nonlinear systems and application to AHS." International Journal of Systems Science 44, no. 2 (February 2013): 321–28. http://dx.doi.org/10.1080/00207721.2011.601344.

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37

Hu, Liyao, and Xiaohua Li. "Decentralised adaptive neural connectively finite-time control for a class of p-normal form large-scale nonlinear systems." International Journal of Systems Science 50, no. 16 (November 22, 2019): 3003–21. http://dx.doi.org/10.1080/00207721.2019.1692095.

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38

Wu, Qiuye, Bo Zhao, and Derong Liu. "Adaptive dynamic programming-based decentralised control for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay." International Journal of Systems Science 51, no. 15 (August 6, 2020): 2883–98. http://dx.doi.org/10.1080/00207721.2020.1803439.

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39

Zhang, Liuliu, Lingchen Zhu, Changchun Hua, and Cheng Qian. "Decentralised state-feedback prescribed performance control for a class of interconnected nonlinear full-state time-delay systems with strong interconnection." International Journal of Systems Science 52, no. 12 (March 2, 2021): 2580–96. http://dx.doi.org/10.1080/00207721.2021.1892237.

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40

Zhou, De-Qun, Yi-Shao Huang, Ke-Jun Long, and Qi-Xin Zhu. "Decentralised direct adaptive output feedback fuzzy controller for a class of large-scale nonaffine nonlinear systems and its applications." International Journal of Systems Science 43, no. 5 (May 2012): 939–51. http://dx.doi.org/10.1080/00207721.2010.543484.

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41

Giannoccaro, Nicola Ivan, and Tetsuzo Sakamoto. "Implementation and Validation of a Simple Direct Identification Method for an Experimental Multi-Span Web Transport System." Systems 10, no. 1 (February 13, 2022): 17. http://dx.doi.org/10.3390/systems10010017.

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Abstract:

The industrial processes that require the use of the web require a control system which allows for preserving the properties of the web unaltered, avoiding the risk of wrinkling, tearing, breakage and other defects. This control generally takes place by detecting the tension and the speed in certain points of the system since these variables determine the stress state on the web, which, if altered beyond certain ranges, can lead to the defects mentioned above. The problem of tension and web speed control is very demanding because the system’s dynamic is a function of many process variables that often vary over a wide range. In this study, an experimental system consisting of 12 rollers, four motorised, was analysed. This system was divided into four subsystems according to the logic of decentralised control. The tension of the initial and final subsystems and the speeds of the two central subsystems were monitored. This study proposes estimating continuous-time transfer functions using experimental time-domain data. A nonlinear least-squares search-based method minimises a weighted prediction error norm for directly identifying the mathematical model used to describe the web transport system. To test the performance of the proposed strategy, experimental data were collected in an open-loop configuration with constant voltage given to the four servo motors. The collected data were subsequently processed to define an extremely simple system model composed of a very limited number of parameters representing the system through transfer functions. The model was further validated by comparing the results obtained through simulations with the experimental data obtained with different inputs, and was also validated with some closed-loop tests.

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42

Bisht, Sanjay, H. S. Bharati, S. B. Taneja, and Punam Bedi. "Command Agent Belief Architecture to Support Commander Decision Making in Military Simulation." Defence Science Journal 68, no. 1 (December 18, 2017): 46. http://dx.doi.org/10.14429/dsj.68.11375.

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Abstract:

<p class="p1">In the war, military conflicts have many aspects that are consistent with complexity theory e.g., the higher commander’s decision is directed at animate entity that react under hierarchical and self-organised structure in decentralised command and control for the collectivist dynamism of decomposed elements due to nonlinear complexity of warfare on the battlefield. Agent technology have been found to be suitable for modelling tactical behaviour of entities at multiple level of resolution under hierarchical command and control (C2) structure and provide a powerful abstraction mechanism required for designing simulations of complex and dynamic battlefield situations. Intelligent agents can potentially reduce the overhead on such experiments and studies. Command agents, plan how to carry out the operation and assign tasks to subordinate agents. They receive information from battlefield environment and use such information to build situation awareness and also to respond to unforeseen situations. In the paper, we have proposed a mechanism for modelling tactical behaviour of an intelligent agent by which higher command level entities should be able to synthesize their beliefs derived from the lower level sub ordinates entities. This paper presents a role-based belief, desire and intention mechanism to facilitate in the representation of military hierarchy, modelling of tactical behaviour based on agent current belief, teammate’s belief propagation, and coordination issues. Higher commander can view the battlefield information at different levels of abstraction based on concept of aggregation and disaggregation and take appropriate reactive response to any unforeseen circumstances happening in battlefield.</p>

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43

Lu, Q., Y. Sun, Z. Xu, and T. Mochizuki. "Decentralized nonlinear optimal excitation control." IEEE Transactions on Power Systems 11, no. 4 (1996): 1957–62. http://dx.doi.org/10.1109/59.544670.

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44

Jamshidi, M., H. Seraji, and Y. T. Kim. "Decentralized control of nonlinear robot manipulators." Robotics 3, no. 3-4 (September 1987): 361–70. http://dx.doi.org/10.1016/0167-8493(87)90053-2.

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45

Abdul-Adheem, Wameedh Riyadh, Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Amjad J. Humaidi. "Improved Active Disturbance Rejection-Based Decentralized Control for MIMO Nonlinear Systems: Comparison with The Decoupled Control Scheme." Applied Sciences 10, no. 7 (April 6, 2020): 2515. http://dx.doi.org/10.3390/app10072515.

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Abstract:

A decentralized control scheme is developed in this paper based on an improved active disturbance rejection control (IADRC) for output tracking of square Multi-Input-Multi-Output (MIMO) nonlinear systems and compared with the decoupled control scheme. These nonlinear MIMO systems were subjected to exogenous disturbances and composed of high couplings between subsystems, input couplings, and uncertain elements. In the decentralized control scheme, it was assumed that the input couplings and subsystem couplings were both parts of the generalized disturbance. Moreover, the generalized disturbance included other components, such as exogenous disturbances and system uncertainties, and it was estimated within the context of Active Disturbance rejection Control (ADRC) via a novel nonlinear higher order extended state observer (NHOESO) from the measured output and canceled from the input channel in a real-time fashion. Then, based on the designed NHOESO, a separate feedback control law was developed for each subsystem to achieve accurate output tracking for given reference input. With the proposed decentralized control scheme, the square MIMO nonlinear system was converted into approximately separate linear time invariant Single-Input-Single-Output (SISO) subsystems. Numerical simulations in a MATLAB environment showed the effectiveness of the proposed technique, where it was applied on a hypothetical MIMO nonlinear system with strong couplings and vast uncertainties. The proposed decentralized control scheme reduced the total control signal energy by 20.8% as compared to the decoupled control scheme using Conventional ADRC (CADRC), while the reduction was 27.18% using the IADRC.

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46

Druzhinina, M. V., and A. L. Fradkov. "Adaptive decentralized control of interconnected nonlinear systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5836–41. http://dx.doi.org/10.1016/s1474-6670(17)56996-7.

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47

Han, Myung-Chul, Keum Shik Hong, Joòngsun Yoon, and Suk Lee. "Decentralized robust control for interconnected nonlinear systems." KSME International Journal 11, no. 1 (January 1997): 1–9. http://dx.doi.org/10.1007/bf02945221.

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48

Ulrich, Steve, and Jurek Z. Sasiadek. "Decentralized simple adaptive control of nonlinear systems." International Journal of Adaptive Control and Signal Processing 28, no. 7-8 (November 21, 2013): 750–63. http://dx.doi.org/10.1002/acs.2446.

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49

Balko, Peter, and Danica Rosinová. "Robust Decentralized Control of Nonlinear Drum Boiler." IFAC-PapersOnLine 48, no. 14 (2015): 432–37. http://dx.doi.org/10.1016/j.ifacol.2015.09.495.

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50

Oketani, Nobuhiro, Toshimitsu Ushio, and Kazumasa Hirai. "Decentralized control of chaos in nonlinear networks." Physics Letters A 198, no. 4 (March 1995): 327–32. http://dx.doi.org/10.1016/0375-9601(94)01025-p.

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