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Journal articles on the topic 'System Linearization'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Bist, Avisek, and Namita Behera. "Fiedler linearizations of multivariable state-space systems and its associated system matrix." Filomat 38, no. 32 (2024): 11275–94. https://doi.org/10.2298/fil2432275b.

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Linearization is a standard method in the computation of eigenvalues and eigenvectors of matrix polynomials. In the last decade a variety of linearization methods have been developed in order to deal with algebraic structures and in order to construct efficient numerical methods. An important source of linearizations for matrix polynomials are the so called Fiedler pencils, which are generalizations of the Frobenius companion form and these linearizations have been extended to regular rational matrix function which is the transfer function of LTI State-space system in [20, 25]. We consider a m

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2

Chao, Kao-Shing Hwang, Horng-Jen. "REINFORCEMENT LINEARIZATION CONTROL SYSTEM." Cybernetics and Systems 31, no. 1 (2000): 115–35. http://dx.doi.org/10.1080/019697200124946.

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3

Brzózka, Jerzy. "Design and Analysis of Model Following Control Structure with Nonlinear Plant." Solid State Phenomena 180 (November 2011): 3–10. http://dx.doi.org/10.4028/www.scientific.net/ssp.180.3.

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Abstract. Linearization methods of the object: input-state and input-output linearization are used usually in a standard feedback control system. However, these systems are sensitive to the changes of nonlinear characteristics of the plant. These changes can be compensated in two types of control systems: in the model following control (MFC) and adaptive. The article presents the first solution and contains: miscellaneous structures of linear control systems with model following, brief description of the linearization’s methods, simulation example of the course control of vessel and the advant

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4

Li, Chunbiao, Julien Clinton Sprott, and Wesley Thio. "Linearization of the Lorenz system." Physics Letters A 379, no. 10-11 (2015): 888–93. http://dx.doi.org/10.1016/j.physleta.2015.01.003.

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5

Lenz, Henning, and Dragan Obradovic. "Robust Control of the Chaotic Lorenz System." International Journal of Bifurcation and Chaos 07, no. 12 (1997): 2847–54. http://dx.doi.org/10.1142/s0218127497001928.

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This paper presents a global approach for controlling the Lorenz system. The basic idea is to partially cancel the nonlinear cross-coupling terms such that the stability of the resulting system can be guaranteed by sequentially proving the stability of each individual state. The method combines ideas from feedback linearization, classical control theory, and Lyapunov's second method. Robust behavior with respect to model uncertainties in the feedback loop is proven. The performance of partial linearization compared to input-state linearization is illustrated on tracking of several trajectories

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6

Zhang, Bin, and Yung C. Shin. "A Data-Driven Approach of Takagi-Sugeno Fuzzy Control of Unknown Nonlinear Systems." Applied Sciences 11, no. 1 (2020): 62. http://dx.doi.org/10.3390/app11010062.

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A novel approach to build a Takagi-Sugeno (T-S) fuzzy model of an unknown nonlinear system from experimental data is presented in the paper. The neuro-fuzzy models or, more specifically, fuzzy basis function networks (FBFNs) are trained from input–output data to approximate the nonlinear systems for which analytical mathematical models are not available. Then, the T-S fuzzy models are derived from the direct linearization of the neuro-fuzzy models. The operating points for linearization are chosen using the evolutionary strategy to minimize the global approximation error so that the T-S fuzzy

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7

Cover, Alan, James Reneke, Suzanne Lenhart, and Vladimir Protopopescu. "RKH Space Methods for Low Level Monitoring and Control of Nonlinear Systems II. A Vector-Case Example: The Lorenz System." Mathematical Models and Methods in Applied Sciences 07, no. 06 (1997): 823–45. http://dx.doi.org/10.1142/s0218202597000426.

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By using techniques from the theory of reproducing kernel Hilbert (RKH) spaces, we continue the exploration of the stochastic linearization method for possibly unknown and/or noise corrupted nonlinear systems. The aim of this paper is twofold: (a) the stochastic linearization formalism is explicitly extended to the vector case; and (b) as an illustration, the performance of the stochastic linearization for monitoring and control is assessed in the case of the Lorenz system for which the dynamic behavior is known independently.

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8

Chang, R. J., and S. J. Lin. "Statistical Linearization Model for the Response Prediction of Nonlinear Stochastic Systems Through Information Closure Method." Journal of Vibration and Acoustics 126, no. 3 (2004): 438–48. http://dx.doi.org/10.1115/1.1688762.

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A new linearization model with density response based on information closure scheme is proposed for the prediction of dynamic response of a stochastic nonlinear system. Firstly, both probability density function and maximum entropy of a nonlinear stochastic system are estimated under the available information about the moment response of the system. With the estimated entropy and property of entropy stability, a robust stability boundary of the nonlinear stochastic system is predicted. Next, for the prediction of response statistics, a statistical linearization model is constructed with the es

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9

Li, W., N. Bartzoudis, JR Fernandez, D. López-Bueno, G. Montoro, and P.L. Gilabert. "FPGA Implementation of a Linearization System for Wideband Envelope Tracking Power Amplifiers." IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 71, no. 4 (2023): 1781–92. https://doi.org/10.1109/TMTT.2022.3217842.

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This article focuses on the implementation of a linearization system for envelope tracking (ET) power amplifiers (PAs) in field-programmable gate array (FPGA). The ET PA linearization system includes a slew-rate reduction envelope generator, a RF leakage cancellation system in the supply envelope path and a baseband I-Q digital predistorter (DPD). This work targets the implementation of an ET PA linearization system on a radio frequency system-on-chip (RFSoC) device running under a demanding baseband sampling frequency of 614.4 MHz, which allows handling communication signals with up to 200 MH

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10

Wang, Dini, Fanwei Meng, and Shengya Meng. "Linearization Method of Nonlinear Magnetic Levitation System." Mathematical Problems in Engineering 2020 (June 22, 2020): 1–5. http://dx.doi.org/10.1155/2020/9873651.

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Linearized model of the system is often used in control design. It is generally believed that we can obtain the linearized model as long as the Taylor expansion method is used for the nonlinear model. This paper points out that the Taylor expansion method is only applicable to the linearization of the original nonlinear function. If the Taylor expansion is used for the derived nonlinear equation, wrong results are often obtained. Taking the linearization model of the maglev system as an example, it is shown that the linearization should be carried out with the process of equation derivation. T

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11

Cardoso, Gildeberto S., and Leizer Schnitman. "Analysis of Exact Linearization and Aproximate Feedback Linearization Techniques." Mathematical Problems in Engineering 2011 (2011): 1–17. http://dx.doi.org/10.1155/2011/205939.

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This paper presents a study of linear control systems based on exact feedback linearization and approximate feedback linearization. As exact feedback linearization is applied, a linear controller can perform the control objectives. The approximate feedback linearization is required when a nonlinear system presents a noninvolutive property. It uses a Taylor series expansion in order to compute a nonlinear transformation of coordinates to satisfy the involutivity conditions.

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12

Lin, Faxing. "Hartman’s linearization on nonautonomous unbounded system." Nonlinear Analysis: Theory, Methods & Applications 66, no. 1 (2007): 38–50. http://dx.doi.org/10.1016/j.na.2005.11.007.

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13

Yong-guang, Yu, and Zhang Suo-chun. "Controlling lü-system using partial linearization." Applied Mathematics and Mechanics 25, no. 12 (2004): 1437–42. http://dx.doi.org/10.1007/bf02438302.

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14

Athorne, C., C. Rogers, U. Ramgulam, and A. Osbaldestin. "On linearization of the Ermakov system." Physics Letters A 143, no. 4-5 (1990): 207–12. http://dx.doi.org/10.1016/0375-9601(90)90740-f.

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15

Diallo, Amadou, and R. Joel Rahn. "Direct linearization of system dynamics models." System Dynamics Review 6, no. 2 (1990): 214–18. http://dx.doi.org/10.1002/sdr.4260060207.

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16

Trebi-Ollennu, A., and B. A. White. "Non-Linear Robust Control Designs for a Remotely Operated Underwater Vehicle Depth Control System." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 210, no. 3 (1996): 201–14. http://dx.doi.org/10.1243/pime_proc_1996_210_455_02.

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This paper describes the heave and pitch control of a non-linear longitudinal model of a remotely operated vehicle (ROV) using three robust non-linear control techniques. The techniques are based on input-output (I/O) linearization, comprising basic I/O linearization control, I/O linearization with sliding mode control and I/O linearization with adaptive fuzzy systems. Robustness of the designs is investigated by examining the closed-loop zero dynamics, which are shown to be bounded. The robustness of each control scheme is also investigated by varying the ROV hydrodynamic coefficients by 50 p

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17

Ayalur Krishnamoorthy, Parvathy, Kamaraj Vijayarajan, and Devanathan Rajagopalan. "Generalized Quadratic Linearization of Machine Models." Journal of Control Science and Engineering 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/926712.

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In the exact linearization of involutive nonlinear system models, the issue of singularity needs to be addressed in practical applications. The approximate linearization technique due to Krener, based on Taylor series expansion, apart from being applicable to noninvolutive systems, allows the singularity issue to be circumvented. But approximate linearization, while removing terms up to certain order, also introduces terms of higher order than those removed into the system. To overcome this problem, in the case of quadratic linearization, a new concept called “generalized quadratic linearizati

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18

Różowicz, Sebastian, and Andrzej Zawadzki. "Input-Output Transformation Using the Feedback of Nonlinear Electrical Circuits: Algorithms and Linearization Examples." Mathematical Problems in Engineering 2018 (November 6, 2018): 1–13. http://dx.doi.org/10.1155/2018/9405256.

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This paper addresses the problem of nonlinear electrical circuit input-output linearization. The transformation algorithms for linearization of nonlinear system through changing coordinates (local diffeomorphism) with the use of closed feedback loop together with the conditions necessary for linearization are presented. The linearization stages and the results of numerical simulations are discussed.

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19

Qian, Hua Ming, Zhen Duo Fu, Liang Chen, and Xiu Li Ning. "Exact Linearization Algorithm of Tight Coupling Nonlinear System Based on Differential Manifold." Applied Mechanics and Materials 380-384 (August 2013): 686–91. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.686.

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Dealt with the short precision the traditional Taylor series expansion induced and shortage that the system mechanics is hard to match with the exact linearization conditions, a novel exact linearization algorithm of tight coupling nonlinear system based on differential manifold to the missile attitude control system is proposed. A dimension expansion method is proposed, the method solves the problem that the input and output dimensions can not meet the exact linearization conditions; the algorithm application range is widened. Using the principle of the differential manifold, the missile velo

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20

Zhang, Ying, Yuntao Shi, Xiang Yin, Meng Zhou, Weichuan Liu, and Daqian Liu. "A Neural Network and Generalized Predictive Control Framework for Urban Traffic System Optimal Perimeter Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 1 (2023): 74–83. http://dx.doi.org/10.20965/jaciii.2023.p0074.

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Optimal perimeter control is one of the effective control technologies to alleviate urban congestion, which is based on macroscopic fundamental diagrams (MFDs). However, most previous optimal perimeter control methods used a linearization model at the desired point to approximate a nonlinear MFDs system, which is vulnerable to causing a potential mismatch between the linearization model and the environmental dynamics. However, this mismatch often leads to performances degradation. To solve this problem, this paper uses a neural network trained from data to approximate the complex nonlinear urb

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21

Shi, Dongwei, and Xiu Yang. "Koopman Spectral Linearization vs. Carleman Linearization: A Computational Comparison Study." Mathematics 12, no. 14 (2024): 2156. http://dx.doi.org/10.3390/math12142156.

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Nonlinearity presents a significant challenge in developing quantum algorithms involving differential equations, prompting the exploration of various linearization techniques, including the well-known Carleman Linearization. Instead, this paper introduces the Koopman Spectral Linearization method tailored for nonlinear autonomous ordinary differential equations. This innovative linearization approach harnesses the interpolation methods and the Koopman Operator Theory to yield a lifted linear system. It promises to serve as an alternative approach that can be employed in scenarios where Carlema

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22

Ciușdel, C. F., S. Coman, Cr Boldișor, et al. "Effect of Linearization in a WNT Signaling Model." Computational and Mathematical Methods in Medicine 2019 (June 10, 2019): 1–9. http://dx.doi.org/10.1155/2019/8461820.

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A nonlinear model consisting of a system of coupled ordinary differential equations (ODE), describing a biological process linked with cancer development, is linearized using Taylor series and tested against different magnitudes of input perturbations, in order to investigate the extent to which the linearization is accurate. The canonical wingless/integrated (WNT) signaling pathway is considered. The linearization procedure is described, and special considerations for linearization validity are analyzed. The analytical properties of nonlinear and linearized systems are studied, including aspe

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23

Zeng, Run Zhang, та Huang Qiu Zhu. "Mathematical Model and Control of Axial Hybrid Magnetic Bearings Based on α-th Order Inverse System Theory". Applied Mechanics and Materials 529 (червень 2014): 539–43. http://dx.doi.org/10.4028/www.scientific.net/amm.529.539.

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A linearization control research based on α-th order inverse system method has been developed for an axial hybrid magnetic bearing, which is a nonlinear system. The configuration of the axial hybrid magnetic bearing is briefly introduced, the working principle of the hybrid magnetic bearing is analyzed, and then the suction equations are set up. Based on expounding of α-th order inverse system method, and aiming at dynamics model of the axial hybrid magnetic bearing, the feasibility of linearization control is discussed in detail, the linearization control arithmetic based on α-th order invers

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24

Savochkin, V. A., and S. M. Shishanov. "Relation Between Features of Harmonic Statistical Linearization." Izvestiya MGTU MAMI 1, no. 2 (2007): 99–105. http://dx.doi.org/10.17816/2074-0530-69600.

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The paper presents the basic computations of method of combined linearization of elastic and damping performance of transport machine cushioning system. This method may be realized if the harmonic linearization coefficient of cushioning system are preliminary determined. This method is based on the possibility to present the differentiable random stationary process as a harmonic signal randomly modulated by phase and amplitude; if relational motion of track roller is a differentiable stationary process, it may be presented as a centered harmonic oscillation. The paper argues that the method of

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25

Liu, Si Jia, Yu Fan, Jun Di, Ya Jing Liu, Wei Qin, and Shuo Li. "Nonlinear Control Approach of an Electromagnetic Bearing System." Applied Mechanics and Materials 538 (April 2014): 387–93. http://dx.doi.org/10.4028/www.scientific.net/amm.538.387.

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This paper proposes a two-electromagnet double degrees of freedom active magnetic bearing (AMB) model. Considering the nonlinearity between the electromagnetic force and the air gap, the authors put forward a nonlinear control approach based on feedback linearization which decouples and linearizes the original system. For the linearized system, pole placement strategy is used to achieve expected steady and dynamic performances. Simulation results show that this approach can successfully implement the decoupling and linearization, and is effective of different initial values.

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26

Li, Na, and Wu Wang. "Feedback Linearization Control for PMSG Wind Power Generation System with Application of Wind Turbine in Mechanical Engineering." Advanced Materials Research 644 (January 2013): 115–18. http://dx.doi.org/10.4028/www.scientific.net/amr.644.115.

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The fluctuation, intermittence and uncertain are the characteristics of wind energy which cause wind energy output power fluctuation, so the wind power generation system was a typical nonlinear system, which was hard to be controlled exactly by traditional controllers, so, the feedback linearization control was applied to wind power generation system (WPGS) in mechanical engineering, which was to reduce the cost of wind energy conversion system and improve its performance. Feedback linearization control contains coordinate transformation, Lie derivative solving, and inverse coordinate transfor

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27

Behera, Namita. "Generalized Fiedler pencils with repetition for rational matrix functions." Filomat 34, no. 11 (2020): 3529–52. http://dx.doi.org/10.2298/fil2011529b.

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We introduce generalized Fiedler pencil with repetition(GFPR) for an n x n rational matrix function G(?) relative to a realization of G(?). We show that a GFPR is a linearization of G(?) when the realization of G(?) is minimal and describe recovery of eigenvectors of G(?) from those of the GFPRs. In fact, we show that a GFPR allows operation-free recovery of eigenvectors of G(?). We describe construction of a symmetric GFPR when G(?) is symmetric. We also construct GFPR for the Rosenbrock system matrix S(?) associated with an linear time-invariant (LTI) state-space system and show that the GFP

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Kao-Shing Hwang and Horag-Jen Chao. "Adaptive reinforcement learning system for linearization control." IEEE Transactions on Industrial Electronics 47, no. 5 (2000): 1185–88. http://dx.doi.org/10.1109/41.873231.

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29

Li, Changpin, and Yutian Ma. "Fractional dynamical system and its linearization theorem." Nonlinear Dynamics 71, no. 4 (2012): 621–33. http://dx.doi.org/10.1007/s11071-012-0601-1.

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30

Prada, Erik, Daxesh Dalal, Darina Hroncová, and Michal Kelemen. "Methods of Linearization of Nonlinear Dynamic System." Acta Mechanica Slovaca 26, no. 2 (2022): 42–47. http://dx.doi.org/10.21496/ams.2021.030.

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31

Sekiguchi, Kazuma, Wataru Eikyu, and Kenichiro Nonaka. "Feedback Control for a Drone with a Suspended Load via Hierarchical Linearization." Journal of Robotics and Mechatronics 33, no. 2 (2021): 274–82. http://dx.doi.org/10.20965/jrm.2021.p0274.

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As a possible extension of a drone application, transportation of a cable-suspended load is expected. The model of a drone with a suspended load is a nonlinear underactuated system that is known to be difficult to analyze and control. This paper applies the linearization method, known as hierarchical linearization, to the system. We observed that, via the hierarchical linearization scheme, the system can be linearized exactly and the controller can be designed simultaneously. There are two features of this approach. First, the controller exactly considers the system nonlinearity, and the feedb

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32

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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33

Xue, Jingwei, Yi Lin, Yuchen Tang, et al. "Sub-synchronous Oscillation Control of the DFIG System with SVG Based on Feedback Linearization of RSC." Journal of Physics: Conference Series 2433, no. 1 (2023): 012001. http://dx.doi.org/10.1088/1742-6596/2433/1/012001.

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Abstract To suppress the subsynchronous oscillation in the doubly-fed induction generator (DFIG) system with Static VAR Generator (SVG), a feedback linearization control (FLC) method for rotor-side converter (RSC) is proposed, and its oscillation effect under small disturbances is analyzed and verified. Firstly, a 22-order time-domain model of a DFIG system with SVG is established. Secondly, combined with the principle of feedback linearization, the corresponding control method is designed for RSC inner loop control. Thirdly, the effects of PI control and feedback linearization control FLC on

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Ayalur Krishnamoorthy, Parvathy, Kamaraj Vijayarajan, and Devanathan Rajagopalan. "Application of Quadratic Linearization for the Control of Permanent Magnet Synchronous Motor." Advances in Power Electronics 2011 (November 16, 2011): 1–11. http://dx.doi.org/10.1155/2011/273081.

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Many of the existing control methods for the permanent magnet synchronous motor (PMSM) either deal with steady state models or consider dynamic models under particular cases. A dynamic model of the PM machine allows powerful control-theoretic techniques such as linearization to be applied to the system. Existing exact feedback linearization of dynamic model of PMSM suffers from singularity issues. In this paper, we propose a quadratic linearization approach for PMSM based on the approximate linearization technique which does not introduce singularities. A MATLAB simulation is used to verify th

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35

Gao, Yuan, and Xiao He Liu. "Feedback Linearization Control of Electric Arc Furnace System Based on dSPACE Simulation." Applied Mechanics and Materials 538 (April 2014): 394–97. http://dx.doi.org/10.4028/www.scientific.net/amm.538.394.

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In this paper, the method of feedback linearization control based on dSPACE simulation for electric arc furnace system is discussed. With the linear feedback method of differential geometry dealing with non-linear part of electric arc furnace system, the controller was designed. Then the hardware-in-the-loop simulation system was built, and several simulations was done. Simulation results show that the feedback linearization control has better performance.

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36

Basu, B., and V. K. Gupta. "On Equivalent Linearization Using Wavelet Transform." Journal of Vibration and Acoustics 121, no. 4 (1999): 429–32. http://dx.doi.org/10.1115/1.2893998.

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This paper proposes a wavelet-based formulation for linearizing a base-excited single-degree-of-freedom nonlinear system to a time-variant linear (TVL) system. The given system is assumed to be nonlinear in stiffness, and the time-dependent natural frequency of the equivalent system is proposed to he estimated through instantaneous minimization of the mean-square error. A duffing oscillator has been considered to illustrate the performance of the proposed TVL system.

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Chang, R. J., and G. E. Young. "Methods and Gaussian Criterion for Statistical Linearization of Stochastic Parametrically and Externally Excited Nonlinear Systems." Journal of Applied Mechanics 56, no. 1 (1989): 179–85. http://dx.doi.org/10.1115/1.3176042.

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The methods of Gaussian linearization along with a new Gaussian Criterion used in the prediction of the stationary output variances of stable nonlinear oscillators subjected to both stochastic parametric and external excitations are presented. The techniques of Gaussian linearization are first derived and the accuracy in the prediction of the stationary output variances is illustrated. The justification of using Gaussian linearization a priori is further investigated by establishing a Gaussian Criterion. The non-Gaussian effects due to system nonlinearities and/or large noise intensities in a

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38

Wang, Qinwei, Zeli Du, Wenting Chen, et al. "Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network." Energies 17, no. 2 (2024): 449. http://dx.doi.org/10.3390/en17020449.

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A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive

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Jeong, Min-Gil, and Ho-Lim Choi. "Switching Control of Electromagnetic Levitation System based on Jacobian Linearization and Input-Output Feedback Linearization." Transactions of The Korean Institute of Electrical Engineers 64, no. 4 (2015): 578–85. http://dx.doi.org/10.5370/kiee.2015.64.4.578.

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40

Ahmed, S. Al-Obeidi, and Fawzi Al-Azzawi Saad. "Chaos synchronization in a 6-D hyperchaotic system with self-excited attractor." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 3 (2020): 1483–90. https://doi.org/10.12928/TELKOMNIKA.v18i3.13672.

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This paper presented stability application for chaos synchronization using a 6-D hyperchaotic system of different controllers and two tools: Lyapunov stability theory and Linearization methods. Synchronization methods based on nonlinear control strategy is used. The selecting controller's methods have been modified by applying complete synchronization. The Linearization methods can achieve convergence according to the of complete synchronization. Numerical simulations are carried out by using MATLAB to validate the effectiveness of the analytical technique.

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41

Khodadadi, Hamed, Hamid Ghadiri, and Ehsan Razavi. "AN OPTIMAL FEEDBACK LINEARIZATION APPROACH FOR SOFT LANDING OF ELECTROMECHANICAL VALVE ACTUATOR IN CAMLESS INTERNAL COMBUSTION ENGINES." Latin American Applied Research - An international journal 50, no. 1 (2019): 47–52. http://dx.doi.org/10.52292/j.laar.2020.88.

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The fuel efficiency, maximum torque and power, and pollutant emissions of internal combustion engines have improved rather than conventional crankshaft system. These improvements are the result of replacing the crankshaft with a camless actuation technology. Soft landing, tracking the desired trajectory, valve seating in a low velocity and having a small time for valve opening and closing are some of the main conflicting objectives in camless engines. Due to the nonlinear model of the system, feedback linearization control is presented for dealing with the system challenges in this paper. More

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42

Firman. "Output Tracking of Some Class Non-Minimum Phase Nonlinear Systems via linearization Input-Output." Journal of Physics: Conference Series 2123, no. 1 (2021): 012014. http://dx.doi.org/10.1088/1742-6596/2123/1/012014.

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Abstract We present an output tracking problem for a non-minimum phase nonlinear system. In this paper, the input control design to solve the output tracking problem is to use the input output linearization method. The use of the input output linearization method cannot be initiated from output causing the system to be non-minimum phase. Therefore the output of the system will be redefined such that the system will become minimum phase with respect to a new output.

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43

Freeman, Walter J. "Neural system stability." Behavioral and Brain Sciences 19, no. 2 (1996): 298–99. http://dx.doi.org/10.1017/s0140525x00042722.

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AbstractTwo hypotheses concerning nonlinear elements in complex systems are contrasted: that neurons, intrinsically unstable, are stabilized through embedding in networks and populations; and, conversely, that cortical neurons are intrinsically stable, but are destabilized through embedding in cortical populations and corticostriatal feedback systems. Tests are made by piecewise linearization of nonlinear dynamics at nonequilibriumoperating points, followed by linear stability analysis.

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44

Pho, Nguyen Van, and Le Ngoc Thach. "Linearization method in reliability problem." Vietnam Journal of Mechanics 15, no. 3 (1993): 37–40. http://dx.doi.org/10.15625/0866-7136/10211.

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Crespo, L. G., and J. Q. Sun. "On the Feedback Linearization of the Lorenz System." Journal of Vibration and Control 10, no. 1 (2004): 85–100. http://dx.doi.org/10.1177/107754604773684911.

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Crespo, L. G., and J. Q. Sun. "On the Feedback Linearization of the Lorenz System." Journal of Vibration and Control 10, no. 1 (2004): 85–100. http://dx.doi.org/10.1177/1077546304030944.

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Abstract:

In this paper we present a control study of the Lorenz system via feedback linearization using the Rayleigh number as a control variable. The effects of the state transformation on the dynamics of the system are studied first. Then, composite controls are derived for both stabilization and tracking problems. The transition through the manifold where the state transformation is singular and the system is insensitive to the control is achieved by inducing the natural chaotic response of the system within a boundary layer. Outside the boundary layer, the control designed via feedback linearizatio

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Rugh, W. "An extended linearization approach to nonlinear system inversion." IEEE Transactions on Automatic Control 31, no. 8 (1986): 725–33. http://dx.doi.org/10.1109/tac.1986.1104385.

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V. Dinh, Nghiep, and Hien N. Nguyen. "Exact Linearization Control for Twin Rotor MIMO System." International Journal of Electrical and Electronics Engineering 3, no. 12 (2016): 40–45. http://dx.doi.org/10.14445/23488379/ijeee-v3i12p107.

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Rourke, David E., and Matthew P. Augustine. "Exact Linearization of the Radiation-Damped Spin System." Physical Review Letters 84, no. 8 (2000): 1685–88. http://dx.doi.org/10.1103/physrevlett.84.1685.

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Kalinowski, Krystian, and Roman Kaula. "Linearization of a Coal Blending Process Control System." IFAC Proceedings Volumes 39, no. 22 (2006): 199–203. http://dx.doi.org/10.1016/s1474-6670(17)30136-2.

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