Relevant bibliographies by topics / MIMO Nonlinear Systems

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Academic literature on the topic 'MIMO Nonlinear Systems'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "MIMO Nonlinear Systems"

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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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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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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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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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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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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 distortion, phase distortion, and the spread of signal constellations. These effects also produce harmonic signals and intermodulation outside the frequency band which results in spectral spread. This will then reduce the performance of the MIMO-GFDM system. The contributions of this paper concern three key areas. Firstly, we investigate the effects of nonlinear distortion on the MIMO-GFDM system. We also derive the new closed-form expression bit error rate (BER) in MIMO-GFDM systems that use a memoryless HPA, which is modeled using the Saleh model when passed through the additive white Gaussian noise (AWGN) channel. This model was chosen because it is simple and has AM/AM and AM/PM curves. Secondly, we propose the application of techniques for the linearization of each HPA predistorter on the transmitter side of the MIMO-GFDM system separately. This predistorter is able to compensate for nonlinear distortion caused by the HPA without memory operating in the saturation region. The main contribution of this paper is to investigate the predistorter, which can linearize nonlinear distortion in MIMO-GFDM transmitters. The performance of the proposed scheme is evaluated in terms of spectrum analysis, PAPR analysis, a constellation diagram, and bit error rate (BER) analysis. The simulation results show that the proposed predistorter design succeeds in compensating for nonlinear distortions caused by the HPA for large input back-off (IBO) values.
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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 by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included.
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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 functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.
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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 structure identifications of the MIMO ARX-Laguerre multiple models are achieved by the recursive method and a metaheuristic algorithm, respectively. The proposed model is built from the system input/output observations and is used to synthesize a MIMO nonlinear fault tolerant control algorithm via MPC. So, we develop a fault detection and isolation (FDI) scheme based on the proposed model. The scheme of the fault detection is applied at every step of MPC control calculation, where we determine the actuator faults and we use it in the MPC optimization problem to determine the new control with respect to the actuator faults. The proposed strategy is tested on numerical simulation and validated on the real system.
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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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Dissertations / Theses on the topic "MIMO Nonlinear Systems"

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Rolim, Fernandes Carlos Alexandre. "Nonlinear MIMO communication systems : channel estimation and information recovery using Volterra models." Nice, 2009. http://www.theses.fr/2009NICE4002.

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Cette thèse introduit de nouvelles méthodes de traitement du signal avec des applications pour les systèmes de communications radio-mobiles. Basés sur les relations de symétrie et de redondance des cumulants d’ordre 4 des signaux de sortie du canal, nous traitons le problème de l’identification aveugle de canaux en utilisant la décomposition Parafac du tenseur des cumulants. Nous développons des algorithmes d’identification aveugles basés sur une minimisation de type moindres carrés à pas unique (SS-LS), permettant d’éviter toute sorte de traitement au préalable. L’approche SS-LS induit une solution bas » sur une seule procédure d’optimisation. En exploitant le concept de réseau virtuel, nous abordons aussi le problème de la localisation de sources dans le cadre d’un réseau d’antennes multiutilisateur, dans le but d’augmenter le nombre de capteurs virtuels pour améliorer la résolution réseau sans faire appel aux statistiques d’ordre 6. Nous traitons par ailleurs le problème de l’estimation des paramètres physiques d’un canal de communication de type MIMO à trajets multiples. En utilisant un formalisme tensoriel, nous proposons une nouvelle technique d’estimation non-paramétrique des coefficient d’un modèle MIMO convolutif, généralisant les méthodes proposées dans les chapitres précédents. Les paramètres du canal sont obtenus à l’aide d’un technique combinée de type ALS-MUSIC, basée sur un algorithme de sous-espaces. Enfin, nous considérons le problème de la détermination d’ordre du canal dans le contexte des systèmes MISO. L’algorithme proposé, basé sur une technique de déflation, détecte les sources, détermine l’ordre des canaux et estime les coefficients associés<br>This thesis introduces new statistical signal processing tools with applications in radio-mobile communication systems. Exploiting the symmetry and redundancy relationships of the 4th-order out-put cumulants, we address the blind channel identification problem by using the Parallel Factor (Parafac) decomposition of the cumulant tensor. We develop blind identification algorithms based on a single-step least squares (SS-LS) minimization problem, enabling us to avoid any kind of pre-processing. The SS-LS approach induces a solution based on a sole optimization procedure. Making use of the Virtual Array concept, we also treat the source localization problem in a multiuser sensor array context in order to provide additional virtual sensors, thus improving the array resolution without resorting to 6th-order statistics. In addition, we consider the problem of estimating the physical parameters of a multipath MIMO communication channel. Using a tensor formalism, we propose a new non-parametric technique to estimate the coefficients of a convolutive MIMO model, so generalizing the methods proposed in the former chapters. We obtain the physical channel parameters by means of a combined ALS-MUSIC technique base on a subspace algorithm. Finally, we turn our attention to the problem of determining the order of FIR channels in the context of MISO systems. The proposed algorithm successively detects the signal sources, determines the order of their individual transmission channels and estimates the associated channel coefficients using a deflationary approach
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Wang, Zhao. "Lyapunov-Based Control Design for Uncertain MIMO Systems." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5558.

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In this dissertation. we document the progress in the control design for a class of MIMO nonlinear uncertain system from five papers. In the first part, we address the problem of adaptive control design for a class of multi-input multi-output (MIMO) nonlinear systems. A Lypaunov based singularity free control law, which compensates for parametric uncertainty in both the drift vector and the input gain matrix, is proposed under the mild assumption that the signs of the leading minors of the control input gain matrix are known. Lyapunov analysis shows global uniform ultimate boundedness (GUUB) result for the tracking error under full state feedback (FSFB). Under the restriction that only the output vector is available for measurement, an output feedback (OFB) controller is designed based on a standard high gain observer (HGO) — stability under OFB is fostered by the uniformity of the FSFB solution. Simulation results for both FSFB and OFB controllers demonstrate the ef?cacy of the MIMO control design in the classical 2-DOF robot manipulator model. In the second part, an adaptive feedback control is designed for a class of MIMO nonlinear systems containing parametric uncertainty in both the drift vector and the input gain matrix, which is assumed to be full-rank and non-symmetric in general. Based on an SDU decomposition of the gain matrix, a singularity-free adaptive tracking control law is proposed that is shown to be globally asymptotically stable (GAS) under full-state feedback. Output feedback results are facilitated via the use of a high-gain observer (HGO). Under output feedback control, ultimate boundedness of the error signals is obtained &"241; the size of the bound is related to the size of the uncertainty in the parameters. An explicit upper bound is also provided on the size of the HGO gain constant. In third part, a class of aeroelastic systems with an unmodeled nonlinearity and external disturbance is considered. By using leading- and trailing-edge control surface actuations, a full-state feedforward/feedback controller is designed to suppress the aeroelastic vibrations of a nonlinear wing section subject to external disturbance. The full-state feedback control yields a uniformly ultimately bounded result for two-axis vibration suppression. With the restriction that only pitching and plunging displacements are measurable while their rates are not, a high-gain observer is used to modify the full-state feedback control design to an output feedback design. Simulation results demonstrate the ef ? cacy of the multi-input multi-output control toward suppressing aeroelastic vibration and limit cycle oscillations occurring in pre and post? utter velocity regimes when the system is subjected to a variety of external disturbance signals. Comparisons are drawn with a previously designed adaptive multi-input multi-output controller. In the fourth part, a continuous robust feedback control is designed for a class of high-order multi-input multi-output (MIMO) nonlinear systems with two degrees of freedom containing unstructured nonlinear uncertainties in the drift vector and parametric uncertainties in the high frequency gain matrix, which is allowed to be non-symmetric in general. Given some mild assumptions on the system model, a singularity-free continuous robust tracking control law is designed that is shown to be semi-globally asymptotically stable under full-state feedback through a Lyapunov stability analysis. The performance of the proposed algorithm have been verified on a two-link robot manipulator model and 2-DOF aeroelastic model.<br>Ph.D.<br>Doctorate<br>Electrical Engineering and Computer Science<br>Engineering and Computer Science<br>Electrical Engineering
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Zeng, Sheng. "Robust Adaptive Control Design for Classes of SISO and MIMO Linear Systems Under Noisy Output Measurements." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1172767648.

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Jiang, Ping, and H. Chen. "Nussbaum gain based iterative learning control for a class of multi-input multi-output nonlinear systems." IEEE, 2005. http://hdl.handle.net/10454/3500.

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Yes<br>An adaptive iterative learning control(ILC) approach is proposed for a class of multi-input multi-output (MIMO) uncertain nonlinear systems without prior knowledge about system control gain matrices. The Nussbaum-type gain and the positive definite discrete matrix kernel are proposed for dealing with selection of the unknown control gain and learning of the repeatable uncertainties, respectively. Asymptotic convergence for a trajectory tracking within a finite time interval is achieved through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.
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Nowak, Xi [Verfasser], and Dirk [Akademischer Betreuer] Söffker. "Data-driven Adaptive Stabilizer for Unknown Nonlinear Dynamic MIMO Systems Using a Cognition-based Framework / Xi Nowak. Betreuer: Dirk Söffker." Duisburg, 2016. http://d-nb.info/1099910234/34.

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Nowak, Xi Verfasser], and Dirk [Akademischer Betreuer] [Söffker. "Data-driven Adaptive Stabilizer for Unknown Nonlinear Dynamic MIMO Systems Using a Cognition-based Framework / Xi Nowak. Betreuer: Dirk Söffker." Duisburg, 2016. http://d-nb.info/1099910234/34.

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Nguyen, Chuong Hoang. "Adaptive Predictor-Based Output Feedback Control of Unknown Multi-Input Multi-Output Systems: Theory and Application to Biomedical Inspired Problems." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/71312.

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Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties.<br>Ph. D.
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Mollén, Christopher. "High-End Performance with Low-End Hardware : Analysis of Massive MIMO Base Station Transceivers." Doctoral thesis, Linköpings universitet, Kommunikationssystem, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-143455.

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Massive MIMO (multiple-input–multiple-output) is a multi-antenna technology for cellular wireless communication, where the base station uses a large number of individually controllable antennas to multiplex users spatially.  This technology can provide a high spectral efficiency.  One of its main challenges is the immense hardware complexity and cost of all the radio chains in the base station.  To make massive MIMO commercially viable, inexpensive, low-complexity hardware with low linearity has to be used, which inherently leads to more signal distortion.  This thesis investigates how the degenerated linearity of some of the main components—power amplifiers, analog-to-digital converters (ADCs) and low-noise amplifiers—affects the performance of the system, with respect to data rate, power consumption and out-of-band radiation. The main results are: Spatial processing can reduce PAR (peak-to-average ratio) of the transmit signals in the downlink to as low as 0B; this, however, does not necessarily reduce power consumption.  In environments with isotropic fading, one-bit ADCs lead to a reduction in effective signal-to-interference-and-noise ratio (SINR) of 4dB in the uplink and four-bit ADCs give a performance close to that of an unquantized system.  An analytical expression for the radiation pattern of the distortion from nonlinear power amplifiers is derived.  It shows how the distortion is beamformed to some extent, that its gain never is greater than that of the desired signal, and that the gain of the distortion is reduced with a higher number of served users and a higher number of channel taps.  Nonlinear low-noise amplifiers give rise to distortion that partly combines coherently and limits the possible SINR.  It is concluded that spatial processing with a large number of antennas reduces the impact of hardware distortion in most cases.  As long as proper attention is paid to the few sources of coherent distortion, the hardware complexity can be reduced in massive MIMO base stations to overcome the hardware challenge and make massive MIMO commercial reality.<br>Massiv MIMO (eng: multiple-input–multiple-output) är en flerantennsteknologi för cellulär trådlös kommunikation, där basstationen använder ett stort antal individuellt styrbara antenner för att multiplexa användare i rummet.  Denna teknologi kan tillhandahålla en hög spektral effektivitet.  En av dess främsta utmaningar är den enorma hårdvarukomplexiteten och kostnaden hos basstationens alla radiokedjor.  För att massiv MIMO skall bli kommersiellt attraktivt, måste billiga, enkla hårdvarukomponenter med låg linjäritet användas, vilket oundvikligen leder till mer signaldistorsion.  Denna avhandling undersöker hur den försämrade linjäriteten hos några av huvudkomponenterna – effektförstärkare, analog-digital-omvandlare (AD-omvandlare) och lågbrusförstärkare – påverkar systemets prestanda, i termer av datatakt, effektförbrukning och utombandsstrålning.  Huvudresultaten är: Rumslig signalbehandling kan reducera sändsignalernas toppvärde i nerlänken ända ner till 0dB, vilket dock inte nödvändigtvis minskar effektförbrukningen.  I miljöer med isotrop fädning leder enbits-AD-omvandlare till 4dB lägre signal-till-interferens-och-brus-förhållande i upplänken, och fyrabits-AD-omvandlare ger en prestanda nära den ett system utan kvantisering kan uppnå.  Ett analytiskt uttryck för strålningsmönstret för distorsionen från icke-linjära effektförstärkare härleds.  Det visar hur distorsionen till viss del lobformas, att dess förstärkning aldrig är starkare än förstärkningen för den önskade signalen och att distorsionens förstärkning minskar med ett högre antal betjänade användare och ett högre antal kanaltappar.  Icke-linjära lågbrusförstärkare ger upphov distorsion som delvis kombinerar koherent och begränsar det möjliga signal-till-brus-och-interferens-förhållandet.  Slutsatsen är att rumslig signalbehandling med ett stort antal antenner reducerar hårdvarudistorsionens inverkan i de flesta fall.  Så länge som de få källorna till koherent distorsion ges tillbörlig uppmärksamhet, kan hårdvarukomplexiteten minskas i basstationer för massiv MIMO för att övervinna hårdvaruutmaningen och göra massiv MIMO kommersiell verklighet.<br>蜂窩無線通訊領域中的大規模多天線技術以多個單獨可控的天線通過空間複用的方式服務多個用戶。如是可以大幅提高頻譜效率。實現此技術的主要難題在於基站所用射頻單元的極大複雜度及成本。爲使大規模多天線技術適用在商業系統中，需使用導致失真的低複雜度低成本的非線性硬件。本文探討若將一些主要部件——功放、模數轉換器、低噪聲放大器——的線性程度降低，系統性能是如何受到影響的，即系統的速率、功耗、帶外泄露等指標。主要的結果爲：空間信號處理可以降低下行信號的峯均比，直至０分貝；然而低峯均比不一定能夠降低功耗。用一比特模數轉換器使上行的信干噪比減少４分貝；用四比特模數轉換器可在各向同性衰落的環境裏實現接近無量化系統的性能。本文推導出非線性功放失真輻射方向的解析公式。該公式展示失真在某種程度上會被波束成形的；具體而言，失真的波束成形增益不大於有效信號的增益，波束成形增益會根據服務用戶數量和信道階數的增長而降低。非線性低噪聲放大器引起的失真，一部分會相干地合併，因此會限制信干噪比的增長。結論爲多天線的空間信號處理可以減少硬件失真的影響。只要適當地處理少數相干失真的來源，大規模多天線基站可以降低硬件複雜度，解決硬件難題，使大規模多天線技術成功地應用在商業系統中成爲現實。
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Vaerenbergh, Steven Van. "Kernel Methods for Nonlinear Identification, Equalization and Separation of Signals." Doctoral thesis, Universidad de Cantabria, 2010. http://hdl.handle.net/10803/10673.

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En la última década, los métodos kernel (métodos núcleo) han demostrado ser técnicas muy eficaces en la resolución de problemas no lineales. Parte de su éxito puede atribuirse a su sólida base matemática dentro de los espacios de Hilbert generados por funciones kernel ("reproducing kernel Hilbert spaces", RKHS); y al hecho de que resultan en problemas convexos de optimización. Además, son aproximadores universales y la complejidad computacional que requieren es moderada. Gracias a estas características, los métodos kernel constituyen una alternativa atractiva a las técnicas tradicionales no lineales, como las series de Volterra, los polinómios y las redes neuronales. Los métodos kernel también presentan ciertos inconvenientes que deben ser abordados adecuadamente en las distintas aplicaciones, por ejemplo, las dificultades asociadas al manejo de grandes conjuntos de datos y los problemas de sobreajuste ocasionados al trabajar en espacios de dimensionalidad infinita.En este trabajo se desarrolla un conjunto de algoritmos basados en métodos kernel para resolver una serie de problemas no lineales, dentro del ámbito del procesado de señal y las comunicaciones. En particular, se tratan problemas de identificación e igualación de sistemas no lineales, y problemas de separación ciega de fuentes no lineal ("blind source separation", BSS). Esta tesis se divide en tres partes. La primera parte consiste en un estudio de la literatura sobre los métodos kernel. En la segunda parte, se proponen una serie de técnicas nuevas basadas en regresión con kernels para resolver problemas de identificación e igualación de sistemas de Wiener y de Hammerstein, en casos supervisados y ciegos. Como contribución adicional se estudia el campo del filtrado adaptativo mediante kernels y se proponen dos algoritmos recursivos de mínimos cuadrados mediante kernels ("kernel recursive least-squares", KRLS). En la tercera parte se tratan problemas de decodificación ciega en que las fuentes son dispersas, como es el caso en comunicaciones digitales. La dispersidad de las fuentes se refleja en que las muestras observadas se agrupan, lo cual ha permitido diseñar técnicas de decodificación basadas en agrupamiento espectral. Las técnicas propuestas se han aplicado al problema de la decodificación ciega de canales MIMO rápidamente variantes en el tiempo, y a la separación ciega de fuentes post no lineal.<br>In the last decade, kernel methods have become established techniques to perform nonlinear signal processing. Thanks to their foundation in the solid mathematical framework of reproducing kernel Hilbert spaces (RKHS), kernel methods yield convex optimization problems. In addition, they are universal nonlinear approximators and require only moderate computational complexity. These properties make them an attractive alternative to traditional nonlinear techniques such as Volterra series, polynomial filters and neural networks.This work aims to study the application of kernel methods to resolve nonlinear problems in signal processing and communications. Specifically, the problems treated in this thesis consist of the identification and equalization of nonlinear systems, both in supervised and blind scenarios, kernel adaptive filtering and nonlinear blind source separation.In a first contribution, a framework for identification and equalization of nonlinear Wiener and Hammerstein systems is designed, based on kernel canonical correlation analysis (KCCA). As a result of this study, various other related techniques are proposed, including two kernel recursive least squares (KRLS) algorithms with fixed memory size, and a KCCA-based blind equalization technique for Wiener systems that uses oversampling. The second part of this thesis treats two nonlinear blind decoding problems of sparse data, posed under conditions that do not permit the application of traditional clustering techniques. For these problems, which include the blind decoding of fast time-varying MIMO channels, a set of algorithms based on spectral clustering is designed. The effectiveness of the proposed techniques is demonstrated through various simulations.
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Rahideh, Akbar. "Model identification and robust nonlinear model predictive control of a twin rotor MIMO system." Thesis, Queen Mary, University of London, 2009. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1885.

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This thesis presents an investigation into a number of model predictive control (MPC) paradigms for a nonlinear aerodynamics test rig, a twin rotor multi-input multi-output system (TRMS). To this end, the nonlinear dynamic model of the system is developed using various modelling techniques. A comprehensive study is made to compare these models and to select the best one to be used for control design purpose. On the basis of the selected model, a state-feedback multistep Newton-type MPC is developed and its stability is addressed using a terminal equality constraint approach. Moreover, the state-feedback control approach is combined with a nonlinear state observer to form an output-feedback MPC. Finally, a robust MPC technique is employed to address the uncertainties of the system. In the modelling stage, analytical models are developed by extracting the physical equations of the system using the Newtonian and Lagrangian approaches. In the case of the black-box modelling, artificial neural networks (ANNs) are utilised to model the TRMS. Finally, the grey-box model is used to enhance the performance of the white-box model developed earlier through the optimisation of parameters using a genetic algorithm (GA) based approach. Stability analysis of the autonomous TRMS is carried out before designing any control paradigms for the system. In the control design stage, an MPC method is proposed for constrained nonlinear systems, which is the improvement of the multistep Newton-type control strategy. The stability of the proposed state-feedback MPC is guaranteed using terminal equality constraints. Moreover, the formerly proposed MPC algorithm is combined with an unscented Kalman filter (UKF) to formulate an output-feedback MPC. An extended Kalman filter (EKF) based on a state-dependent model is also introduced, whose performance is found to be better compared to that of the UKF. Finally, a robust MPC is introduced and implemented on the TRMS based on a polytopic uncertainty that is cast into linear matrix inequalities (LMI).
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Books on the topic "MIMO Nonlinear Systems"

1

Swain, A. K. Generalised frequency response function matrix for MIMO nonlinear systems. University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1996.

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Billings, S. A. Identification of mimo nonlinear systems using a forward-regression orthogonal estimator. University of Sheffield, Dept. of Control Engineering, 1988.

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Song, Dong, and Theodore W. Berger. Hippocampal memory prosthesis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0055.

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Damage to the hippocampus and surrounding regions of the medial temporal lobe can result in a permanent loss of the ability to form new long-term memories. Hippocampal memory prosthesis is designed to restore this ability. The animal model described here is the memory-dependent, delayed nonmatch-to-sample (DNMS) task in rats, and the core of the prosthesis is a biomimetic multi-input, multi-output (MIMO) nonlinear dynamical model that predicts hippocampal output (CA1) signals based on input (CA3) signals. When hippocampal CA1 function is pharmacologically blocked, successful DNMS behavior is abolished. However, when MIMO model predictions are used to re-instate CA1 memory-related activities with electrical stimulation, successful DNMS behavior and long-term memory function are restored. The hippocampal memory prosthesis has been successfully implemented in rodents and nonhuman primates, but the current system requires major advances before it can approach a working prosthesis. Looking forward, a deeper knowledge of neural coding will provide further insights.
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Book chapters on the topic "MIMO Nonlinear Systems"

1

Song, Yongduan, Kai Zhao, and Hefu Ye. "Control of MIMO Systems." In Control of Nonlinear Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781003474364-7.

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Fradkov, Alexander L., Iliya V. Miroshnik, and Vladimir O. Nikiforov. "Nonlinear Control of MIMO Systems." In Nonlinear and Adaptive Control of Complex Systems. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9261-1_5.

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Montezuma, Paulo, João Guerreiro, and Rui Dinis. "MIMO-OFDM and Optical OFDM Systems." In Unified Theoretical Analysis of Nonlinear Multicarrier Schemes. CRC Press, 2024. http://dx.doi.org/10.1201/9781032708744-6.

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Lurie, Boris J., and Paul J. Enright. "Feedforward, Multi-Loop, and MIMO Systems." In Classical Feedback Control with Nonlinear Multi-Loop Systems. CRC Press, 2019. http://dx.doi.org/10.1201/9781351011853-2.

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Yaniv, Oded. "Synthesis of LTI Controllers for Nonlinear MIMO Plants." In Quantitative Feedback Design of Linear and Nonlinear Control Systems. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-6331-7_7.

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Yaniv, Oded. "Synthesis of LTV Controllers for Nonlinear MIMO Plants." In Quantitative Feedback Design of Linear and Nonlinear Control Systems. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-6331-7_8.

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Ren, Hao, Gang Zhu, and Yan Lin. "Adaptive Compensation for MIMO Nonlinear Systems Against Actuator Failures." In Proceedings of 2018 Chinese Intelligent Systems Conference. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2288-4_37.

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Yaniv, Oded. "Synthesis of LTI Controllers for MIMO LTI Plants." In Quantitative Feedback Design of Linear and Nonlinear Control Systems. Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-6331-7_4.

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Csurcsia, Péter Zoltán, Bart Peeters, and Johan Schoukens. "The Best Linear Approximation of MIMO Systems: First Results on Simplified Nonlinearity Assessment." In Nonlinear Structures and Systems, Volume 1. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12391-8_6.

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Esfandiari, Kasra, Farzaneh Abdollahi, and Heidar A. Talebi. "NN-Based Adaptive Control of MIMO Nonaffine Noncanonical Nonlinear Systems." In Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73136-6_6.

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Conference papers on the topic "MIMO Nonlinear Systems"

1

Liu, Yun-Qi, and Jin-Xi Zhang. "Global Prescribed Performance Control of MIMO Nonlinear Systems." In 2024 6th International Conference on Industrial Artificial Intelligence (IAI). IEEE, 2024. http://dx.doi.org/10.1109/iai63275.2024.10730382.

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Lu, Weiqi, Yuhao Fang, Yang Zou, et al. "Dynamic Nonlinear MIMO Equalization in 3-D Polarization Multiplexed Direct Detection Systems." In Optical Fiber Communication Conference. Optica Publishing Group, 2025. https://doi.org/10.1364/ofc.2025.th1k.5.

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Kim, Yeesock, Stefan Hurlebaus, Reza Sharifi, and Reza Langari. "Nonlinear Identification of MIMO Smart Structures." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2715.

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This paper proposes a multiple model approach for a nonlinear multi-input, multi-output (MIMO) system identification (SI) of smart structures equipped with magnetorheological (MR) dampers. The proposed model is developed through integration of MIMO autoregressive exogenous (ARX) input models, Takagi-Sugeno (TS) fuzzy model, weighted linear least squares estimators, and data clustering algorithms. Nonlinear behavior of the structure-MR damper systems is represented by a set of linear MIMO ARX input models whose operating regions are blended by TS fuzzy sets. To demonstrate the effectiveness of the proposed MIMO ARX-TS fuzzy model, a 20-story high-rise building employing MR dampers is investigated. It is demonstrated that the proposed approach is effective in modeling nonlinear behavior of the structure-MR damper system subjected to a variety of disturbances. Comparison with high fidelity data proves the viability of the proposed approach in control engineering setting.
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Zhao, Chen, Yuhao Zhou, and Xin Wang. "Adaptive Control for Nonlinear MIMO Systems." In 2022 International Conference on Intelligent Systems and Computational Intelligence (ICISCI). IEEE, 2022. http://dx.doi.org/10.1109/icisci53188.2022.9941451.

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Shi, Wuxi, Shaoping Chang, and Mingxia Bao. "Adaptive fuzzy control for MIMO nonlinear systems." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162059.

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Lin, C. M., T. Y. Chen, C. H. Chen, and F. S. Ding. "Fuzzy CMAC control for MIMO nonlinear systems." In 2007 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icsmc.2007.4413653.

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Haeri, M., and H. Z. Beik. "Extension of Nonlinear DMC for MIMO Systems." In 4th International Conference on Control and Automation. Final Program and Book of Abstracts. IEEE, 2003. http://dx.doi.org/10.1109/icca.2003.1595048.

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McGregor, N. K., C. I. Byrnes, and A. Isidori. "Results on nonlinear output regulation for MIMO systems." In 2006 American Control Conference. IEEE, 2006. http://dx.doi.org/10.1109/acc.2006.1657649.

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Harris, K. R., and A. Palazoglu. "Control of nonlinear MIMO systems via functional expansions." In Proceedings of 16th American CONTROL Conference. IEEE, 1997. http://dx.doi.org/10.1109/acc.1997.610882.

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Azimi, Vahid, Mohammad Bagher Menhaj, and Ahmad Fakharian. "Fuzzy robust control of MIMO nonlinear uncertain systems." In 2013 13th Iranian Conference on Fuzzy Systems (IFSC). IEEE, 2013. http://dx.doi.org/10.1109/ifsc.2013.6675640.

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Reports on the topic "MIMO Nonlinear Systems"

1

Tatlicioglu, Enver, Bin Xian, Darren M. Dawson, and Timothy Burg. Adaptive Control of Flat MIMO Nonlinear Systems with Additive Disturbance. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada462618.

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