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Journal articles on the topic 'MIMO Nonlinear Systems'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Samardžić, Biljana, and Bojana Zlatković. "MODIFIED PYRAGAS METHOD FOR MULTIPLE SPATIAL LIMIT SETS AND CHAOS CONTROL IN MIMO CASCADE NONLINEAR SYSTEMS." Facta Universitatis, Series: Automatic Control and Robotics 17, no. 3 (2019): 165. http://dx.doi.org/10.22190/fuacr1803165s.

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The modified Pyragas method for the multiple spatial limit sets and chaos control in MIMO cascade nonlinear systems is presented in this paper. Also, oscillatory and chaotic dynamic analysis of concrete MIMO3 cascade nonlinear system is performed before and after the application of control. Bifurcation diagrams and spatial phase portraits of uncontrolled and controlled MIMO3 system are used for the purposes of analysis.

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2

Mangsuli, Purnaprajna R., and N. J. Rao. "Nonlinear Luenberger-like observers for nonlinear MIMO systems." Asian Journal of Control 10, no. 4 (2008): 495–506. http://dx.doi.org/10.1002/asjc.49.

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3

Wang, Lei, Alberto Isidori, Hongye Su, and Lorenzo Marconi. "Nonlinear output regulation for invertible nonlinear MIMO systems." International Journal of Robust and Nonlinear Control 26, no. 11 (2015): 2401–17. http://dx.doi.org/10.1002/rnc.3454.

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4

Fossen, Thor I., and Bjarne A. Foss. "Sliding control of MIMO nonlinear systems." Modeling, Identification and Control: A Norwegian Research Bulletin 12, no. 3 (1991): 129–38. http://dx.doi.org/10.4173/mic.1991.3.3.

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5

Lee, S., and M. Park. "State observer for MIMO nonlinear systems." IEE Proceedings - Control Theory and Applications 150, no. 4 (2003): 421–26. http://dx.doi.org/10.1049/ip-cta:20030513.

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6

Jayati, Ari Endang, Wirawan, Titiek Suryani, and Endroyono. "Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems." Electronics 8, no. 6 (2019): 620. http://dx.doi.org/10.3390/electronics8060620.

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Generalized frequency division multiplexing (GFDM) with offset quadrature amplitude modulation (OQAM) is an alternative non-orthogonal modulation scheme for future generation wireless broadband systems. The nonlinearity of high power amplifiers (HPAs) has a very significant effect on the performance of GFDM systems. In this paper, we investigate the effects of nonlinear distortion on the multiple-input multiple-output (MIMO)-GFDM system when the signal is passed the HPA, which is modeled with amplitude and phase distortion. The effects of nonlinear distortion due to the HPA include amplitude d

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7

Abdul-Adheem, Wameedh Riyadh, Ahmed Alkhayyat, Ammar K. Al Mhdawi, et al. "Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems." Entropy 23, no. 11 (2021): 1487. http://dx.doi.org/10.3390/e23111487.

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Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed

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8

Zhen, Hong-tao, Xiao-hui Qi, Jie Li, and Qing-min Tian. "Neural NetworkL1Adaptive Control of MIMO Systems with Nonlinear Uncertainty." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/942094.

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An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of anL1adaptive controller and an auxiliary neural network (NN) compensation controller. TheL1adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functi

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9

Mbarek, Abdelkader, and Kais Bouzrara. "Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX-Laguerre Multiple Models." Mathematical Problems in Engineering 2019 (October 3, 2019): 1–26. http://dx.doi.org/10.1155/2019/9012182.

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In this article, we propose a fault tolerant control for multiple-input multiple-output (MIMO) nonlinear systems via model predictive control. The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models. The latter is obtained by expanding a discrete-time MIMO ARX multiple model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical MIMO ARX multiple models. This parametric complexity reduction still subjects to an optimal choice of the Laguerre poles defining Laguerre bases. The parameter and s

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10

Zhang, Huaguang, and Zeungnam Bien. "Adaptive fuzzy control of MIMO nonlinear systems." Fuzzy Sets and Systems 115, no. 2 (2000): 191–204. http://dx.doi.org/10.1016/s0165-0114(98)00394-7.

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11

Dobrowiecki, Tadeusz P., and Johan Schoukens. "Linear Approximation of Weakly Nonlinear MIMO Systems." IEEE Transactions on Instrumentation and Measurement 56, no. 3 (2007): 887–94. http://dx.doi.org/10.1109/tim.2007.894898.

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12

Aggarwal, Parag, and Vivek Ashok Bohara. "A Nonlinear Downlink Multiuser MIMO-OFDM Systems." IEEE Wireless Communications Letters 6, no. 3 (2017): 414–17. http://dx.doi.org/10.1109/lwc.2017.2699195.

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13

Abdusamad, Abdunaser M., Farag I. k. Mousa, and Abdulhakim A. M. Daluom. "Optimizing Feedback Linearization for MIMO Nonlinear Tracking Control Using Input-Output and Input-State Techniques." International Science and Technology Journal 36, no. 2 (2025): 1–19. https://doi.org/10.62341/afao2049.

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systems to convert the dynamics of nonlinear systems into a linear format, simplifying their analysis and control. However, managing highly nonlinear systems can be both challenging and complex. This paper seeks to address this challenge by proposing an enhanced approach to the feedback linearization technique. To improve the feedback linearization tracking control of multi-input multi-output (MIMO) nonlinear systems, the paper investigates two primary strategies. The first strategy involves adjusting control gains mathematically along with other parameters to optimize control performance, ena

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14

Luis-Delgado, José Darío, Basil Mohammed Al-Hadithi, and Agustín Jiménez. "A Novel Method for the Design of Switching Surfaces for Discretized MIMO Nonlinear Systems." International Journal of Applied Mathematics and Computer Science 27, no. 1 (2017): 5–17. http://dx.doi.org/10.1515/amcs-2017-0001.

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Abstract Designing variable structure control with sliding mode (VSC-SM) control schemes needs a switching function or a sliding surface which guarantees the global stability of the closed-loop system. Despite the fact that a wide range of design approaches has been proposed for solving this mathematical problem, the number of proposed methodologies for nonlinear systems is not very extensive, especially for discrete time nonlinear MIMO systems, and most of them require some coordinate system transformation. Therefore, it is not an easy task to find a design scheme that can be applied to discr

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15

Lyu, Hongli, Yanan Lyu, Yongchao Gao, Heng Qian, and Shan Du. "MIMO fuzzy adaptive control systems based on fuzzy semi-tensor product." Mathematical Modelling and Control 3, no. 4 (2023): 316–30. http://dx.doi.org/10.3934/mmc.2023026.

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<abstract><p>Based on fuzzy semi-tensor product (STP) algorithms and fuzzy relation matrix (FRM) models, the design of an adaptive fuzzy controller was proposed in this paper for the multivariable nonlinear systems with uncertainty. The controlled multi-input-and-multi-output (MIMO) plants were expressed and processed first by FRM models and fuzzy STP operations, and then the indirect adaptive fuzzy control laws were designed. The tracking property of the FRM models was proved for the control objective of MIMO systems. The effectiveness of the novel matrix expression was verified b

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16

Hassan, Emad S. "Performance enhancement and PAPR reduction for MIMO based QAM-FBMC systems." PLOS ONE 19, no. 1 (2024): e0296999. http://dx.doi.org/10.1371/journal.pone.0296999.

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Filter Bank Multi-Carrier (FBMC) is attracting significant interest as a multi-carrier modulation (MCM) approach for future communication systems. It offers numerous advantages in contrast to Orthogonal Frequency Division Multiplexing (OFDM). Nonetheless, similar to many other MCM techniques, FBMC encounters a significant challenge with a high Peak-to-Average Power Ratio (PAPR). Additionally, incorporating Multiple-Input and Multiple-Output (MIMO) into FBMC presents heightened difficulties due to the presence of complex interference and increased computational complexity. In this paper, we fir

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17

Ho, Anh Pham Huy, and Nam Thanh Nguyen. "Identification of Mimo dynamic system using inverse Mimo Neural Narx model." Science and Technology Development Journal 16, no. 2 (2013): 13–25. http://dx.doi.org/10.32508/stdj.v16i2.1506.

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This paper investigates the application of proposed neural MIMO NARX model to a nonlinear 2-axes pneumatic artificial muscle (PAM) robot arm as to improve its performance in modeling and identification. The contact force variations and nonlinear coupling effects of both joints of the 2-axes PAM robot arm are modeled thoroughly through the novel dynamic inverse neural MIMO NARX model exploiting experimental input-output training data. For the first time, the dynamic neural inverse MIMO NARX Model of the 2-axes PAM robot arm has been investigated. The results show that this proposed dynamic inte

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18

Mohammed A. Ali, Musaab, and Volodymyr Vodichev. "MODELING OF MIMO SYSTEMS WITH UNIVERSAL CONTROLLER." ELECTRICAL AND COMPUTER SYSTEMS 37, no. 113 (2023): 26–32. http://dx.doi.org/10.15276/eltecs.37.113.2023.03.

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Abstract. The control of multi-input multi-output (MIMO) systems poses significant challenges due to the intricate dynamics and interactions between multiple input and output channels. This paper presents a simulation study on MIMO systems control using the universal control technique. The proposed method offers a versatile framework for regulating MIMO systems with varying dynamics, utilizing a single control algorithm. The underlying concept of the universal control law is to devise a set of principles or strategies that can be universally applied to any system, regardless of its specific ch

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19

Wang, Lan, Yu Cheng, and Jinglu Hu. "A Quasi-ARX Model for Multivariable Decoupling Control of Nonlinear MIMO System." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/570498.

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This paper proposes a multiinput and multioutput (MIMO) quasi-autoregressive eXogenous (ARX) model and a multivariable-decoupling proportional integral differential (PID) controller for MIMO nonlinear systems based on the proposed model. The proposed MIMO quasi-ARX model improves the performance of ordinary quasi-ARX model. The proposed controller consists of a traditional PID controller with a decoupling compensator and a feed-forward compensator for the nonlinear dynamics based on the MIMO quasi-ARX model. Then an adaptive control algorithm is presented using the MIMO quasi-ARX radial basis

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20

WU, Ligang, Siyi WANG, Yabin GAO, Jianxing LIU, and Guanghui SUN. "Constrained sliding mode control of MIMO nonlinear systems." SCIENTIA SINICA Informationis 51, no. 3 (2021): 430. http://dx.doi.org/10.1360/ssi-2020-0162.

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21

Kreyndelin, V. B., and T. B. K. Ben Rejeb. "Nonlinear iterative precoding algorithm for MIMO multiuser systems." Radioelectronics and Communications Systems 60, no. 10 (2017): 449–57. http://dx.doi.org/10.3103/s073527271710003x.

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22

Ge, S. S., and C. Wang. "Adaptive Neural Control of Uncertain MIMO Nonlinear Systems." IEEE Transactions on Neural Networks 15, no. 3 (2004): 674–92. http://dx.doi.org/10.1109/tnn.2004.826130.

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23

Guerreiro, Joao, Rui Dinis, Paulo Montezuma, and Mario Marques da Silva. "On the Achievable Performance of Nonlinear MIMO Systems." IEEE Communications Letters 23, no. 10 (2019): 1725–29. http://dx.doi.org/10.1109/lcomm.2019.2929040.

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24

Sandell, Magnus, Henning Vetter, and Filippo Tosato. "Joint Linear and Nonlinear Precoding in MIMO Systems." IEEE Communications Letters 15, no. 12 (2011): 1265–67. http://dx.doi.org/10.1109/lcomm.2011.103111.110542.

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25

Dobrowiecki, Tadeusz, and Johan Schoukens. "MEASURING LINEAR APPROXIMATION TO WEAKLY NONLINEAR MIMO SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 1173–78. http://dx.doi.org/10.3182/20050703-6-cz-1902.00197.

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26

Qin, Huashu, Shengwei Mei, Quanmin Zhu, and Yiguang Hong. "Robust Attraction for Mimo Affine Nonlinear Uncertain Systems." IFAC Proceedings Volumes 29, no. 1 (1996): 2538–43. http://dx.doi.org/10.1016/s1474-6670(17)58056-8.

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27

Liu, Xiaoping, Guoxiang Gu, and Kemin Zhou. "Robust stabilization of MIMO nonlinear systems by backstepping." Automatica 35, no. 5 (1999): 987–92. http://dx.doi.org/10.1016/s0005-1098(98)00236-2.

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28

Agarwal, Mukul, and Dale E. Seborg. "A Self-tuning Controller for MIMO Nonlinear Systems." IFAC Proceedings Volumes 20, no. 2 (1987): 119–24. http://dx.doi.org/10.1016/s1474-6670(17)55947-9.

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29

Miroshnik, Iliya. "Functional decomposition and nonlinear control of MIMO systems." IFAC Proceedings Volumes 32, no. 2 (1999): 5860–65. http://dx.doi.org/10.1016/s1474-6670(17)57000-7.

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30

Shi, Wuxi, Mu Zhang, Wencheng Guo, and Lijin Guo. "Stable adaptive fuzzy control for MIMO nonlinear systems." Computers & Mathematics with Applications 62, no. 7 (2011): 2843–53. http://dx.doi.org/10.1016/j.camwa.2011.07.050.

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31

Wang, Lei, Alberto Isidori, Zhitao Liu, and Hongye Su. "Robust output regulation for invertible nonlinear MIMO systems." Automatica 82 (August 2017): 278–86. http://dx.doi.org/10.1016/j.automatica.2017.04.049.

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32

Lin, W. S., and C. H. Tsai. "Neurofuzzy-model-following control of MIMO nonlinear systems." IEE Proceedings - Control Theory and Applications 146, no. 2 (1999): 157–64. http://dx.doi.org/10.1049/ip-cta:19990515.

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33

Chen, Chiu-Hsiung, Chih-Min Lin, and Te-Yu Chen. "Intelligent adaptive control for MIMO uncertain nonlinear systems." Expert Systems with Applications 35, no. 3 (2008): 865–77. http://dx.doi.org/10.1016/j.eswa.2007.07.053.

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34

Ogonowski, Z. "Application of Predictive Control to Nonlinear MIMO Systems." IFAC Proceedings Volumes 27, no. 11 (1994): 105–10. http://dx.doi.org/10.1016/s1474-6670(17)47631-2.

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35

Haeri, Mohammad, and Hossein Zadehmorshed Beik. "Application of extended DMC for nonlinear MIMO systems." Computers & Chemical Engineering 29, no. 9 (2005): 1867–74. http://dx.doi.org/10.1016/j.compchemeng.2005.03.003.

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36

Zhao, Zhi-Liang, Ruonan Yuan, Bao-Zhu Guo, and Zhong-ping Jiang. "Finite-Time Stabilization of Nonlinear Uncertain MIMO Systems." SIAM Journal on Control and Optimization 61, no. 4 (2023): 2305–33. http://dx.doi.org/10.1137/22m1496086.

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37

Mehrafrooz, Arash, Fangpo He, and Ali Lalbakhsh. "Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems." Sensors 22, no. 6 (2022): 2089. http://dx.doi.org/10.3390/s22062089.

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In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weigh

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38

Hopkins, M. A., and H. F. VanLandingham. "Optimal Nonlinear Estimation of Linear Stochastic Systems: The Multivariable Extension." Journal of Dynamic Systems, Measurement, and Control 118, no. 2 (1996): 350–53. http://dx.doi.org/10.1115/1.2802328.

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This paper extends to multi-input multi-output (MIMO) systems a nonlinear method of simultaneous parameter and state estimation that appeared in the ASME JDSM&C (September, 1994), for single-input single-output (SISO) systems. The method is called pseudo-linear identification (PLID), and applies to stochastic linear time-invariant discrete-time systems. No assumptions are required about pole or zero locations; nor about relative degree, except that the system transfer functions must be strictly proper. In the earlier paper, proofs of optimality and convergence were given. Extensions of tho

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39

Chen, Menghui, Xiaoshu Gao, Cheng Chen, Tong Guo, and Weijie Xu. "A Comparative Study of Meta-Modeling for Response Estimation of Stochastic Nonlinear MDOF Systems Using MIMO-NARX Models." Applied Sciences 12, no. 22 (2022): 11553. http://dx.doi.org/10.3390/app122211553.

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Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative for a large number of MCS. The previous study has demonstrated that the multi-input multi-output nonlinear autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representations of deterministic nonlinear multi-degree-of-freedom (MDOF) structural dynamic systems. Model order reduction (MOR) is executed to eliminate insignificant modes to reduce the computat

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40

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 (2020): 2515. http://dx.doi.org/10.3390/app10072515.

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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

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41

Chee, Ewan, and Xiaonan Wang. "Generalized System Identification for Nonlinear MPC of Highly Nonlinear MIMO Systems." IFAC-PapersOnLine 54, no. 3 (2021): 366–71. http://dx.doi.org/10.1016/j.ifacol.2021.08.269.

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42

El Ghzaoui, M., A. Hmamou, J. Foshi, and J. Mestoui. "Compensation of Non-linear Distortion Effects in MIMO-OFDM Systems Using Constant Envelope OFDM for 5G Applications." Journal of Circuits, Systems and Computers 29, no. 16 (2020): 2050257. http://dx.doi.org/10.1142/s0218126620502576.

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Orthogonal frequency division multiplexing (OFDM) is a multicarrier transmission system that can achieve high data rate over wireless channels. At the same time, multiple input multiple output OFDM (MIMO-OFDM) in wireless communication systems has been exposed to offer significant improvement over wireless technology by providing transmit diversity. It has become a promising technique for high-performance 5G broadband wireless communications. However, the main problem associated with MIMO-OFDM is that its signal exhibits high peak-to-average power ratio (PAPR), which causes nonlinear distortio

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43

DUAN, ZHISHENG, JINZHI WANG, and LIN HUANG. "MULTI-INPUT AND MULTI-OUTPUT NONLINEAR SYSTEMS: INTERCONNECTED CHUA'S CIRCUITS." International Journal of Bifurcation and Chaos 14, no. 09 (2004): 3065–81. http://dx.doi.org/10.1142/s0218127404011168.

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In this paper, a class of MIMO nonlinear systems are studied. Some frequency domain conditions are established for the property of dichotomy. These kinds of systems can also be viewed as a class of interconnected systems composed of SISO systems through some linear and nonlinear interconnections. A class of nonlinear input and output interconnections are presented. The corresponding condition for testing dichotomy is given. Furthermore, Chua's circuit and interconnected Chua's circuit are studied to illustrate the theoretical results.

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44

Feng, W., D. J. Ballance, and J. O'Reilly. "MIMO nonlinear PID predictive controller." IEE Proceedings - Control Theory and Applications 149, no. 3 (2002): 203–8. http://dx.doi.org/10.1049/ip-cta:20020339.

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45

Liu, Ying, Hui Wang, Quanxin Zhu, and Fujin Jia. "Zero-Error Prescribed-Time Tracking Control for Switched Non-Square MIMO Nonlinear Systems Subject to Asymmetric Time-Varying Output Constraints." Mathematics 13, no. 6 (2025): 986. https://doi.org/10.3390/math13060986.

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Previous studies typically assume that output constraints are symmetric or time-invariant. However, effectively addressing asymmetric and time-varying output constraints remains an unsolved issue, especially in the case of switched non-square multi-input multi-output (MIMO) nonlinear systems. To tackle this challenge, this paper first establishes a prescribed-time (PT) Lyapunov criterion for switched nonlinear systems. Second, an asymmetric nonlinear mapping (ANM) method is proposed to handle asymmetric time-varying output constraints. Compared to the barrier Lyapunov function (BLF) approach,

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46

Choi, Han Ho. "LMI-Based Nonlinear Fuzzy Observer-Controller Design for Uncertain MIMO Nonlinear Systems." IEEE Transactions on Fuzzy Systems 15, no. 5 (2007): 956–71. http://dx.doi.org/10.1109/tfuzz.2006.890676.

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47

Kotta, Ülle, Branislav Rehak, and Malgorzata Wyrwas. "Reduction of MIMO nonlinear systems on homogeneous time scales." IFAC Proceedings Volumes 43, no. 14 (2010): 1249–54. http://dx.doi.org/10.3182/20100901-3-it-2016.00007.

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48

Kvaternik, Karla, and Alan F. Lynch. "Global Tracking via Output Feedback for Nonlinear MIMO Systems." IEEE Transactions on Automatic Control 56, no. 9 (2011): 2179–84. http://dx.doi.org/10.1109/tac.2011.2158134.

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49

Mon, Yi-Jen, Chih-Min Lin, and Chin-Hsu Leng. "Recurrent Fuzzy Neural Network Control for Mimo Nonlinear Systems." Intelligent Automation & Soft Computing 14, no. 4 (2008): 395–415. http://dx.doi.org/10.1080/10798587.2008.10643002.

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50

Lin, Chun-Tao, Fan-Shuo Tseng, Wen-Rong Wu, and Ronald Y. Chang. "Nonlinear Transceiver Designs for Full-Duplex MIMO Relay Systems." IEEE Transactions on Communications 65, no. 11 (2017): 4632–45. http://dx.doi.org/10.1109/tcomm.2017.2741959.

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