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Relevant bibliographies by topics / Form Brunovsky

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Journal articles

Academic literature on the topic 'Form Brunovsky'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 May 2022

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Journal articles on the topic "Form Brunovsky"

1

Yakovenko, Gennadii Nikolaevich. "Control systems in Brunovsky form: symmetries, controllability." Computer Research and Modeling 1, no. 2 (2009): 147–59. http://dx.doi.org/10.20537/2076-7633-2009-1-2-147-159.

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2

Baragaña, Itziar, M. Asunción Beitia, and Inmaculada de Hoyos. "Structured perturbation of the Brunovsky form: A particular case." Linear Algebra and its Applications 430, no. 5-6 (2009): 1613–25. http://dx.doi.org/10.1016/j.laa.2008.05.022.

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3

THEODORIDIS, DIMITRIOS, YIANNIS BOUTALIS, and MANOLIS CHRISTODOULOU. "A NEW DIRECT ADAPTIVE REGULATOR WITH ROBUSTNESS ANALYSIS OF SYSTEMS IN BRUNOVSKY FORM." International Journal of Neural Systems 20, no. 04 (2010): 319–39. http://dx.doi.org/10.1142/s0129065710002449.

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The direct adaptive regulation of unknown nonlinear dynamical systems in Brunovsky form with modeling error effects, is considered in this paper. Since the plant is considered unknown, we propose its approximation by a special form of a Brunovsky type neuro–fuzzy dynamical system (NFDS) assuming also the existence of disturbance expressed as modeling error terms depending on both input and system states plus a not-necessarily-known constant value. The development is combined with a sensitivity analysis of the closed loop and provides a comprehensive and rigorous analysis of the stability prope

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4

Kamachkin, Alexander M., Nikolai A. Stepenko, and Gennady M. Chitrov. "On the theory of constructive construction of a linear controller." Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes 16, no. 3 (2020): 326–44. http://dx.doi.org/10.21638/11701/spbu10.2020.309.

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The classical problem of stationary stabilization with respect to the state of a linear stationary control system is investigated. Efficient, easily algorithmic methods for constructing controllers of controlled systems are considered: the method of V. I. Zubov and the method of P. Brunovsky. The most successful modifications are indicated to facilitate the construction of a linear controller. A new modification of the construction of a linear regulator is proposed using the transformation of the matrix of the original system into a block-diagonal form. This modification contains all the advan

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5

Gardner, R. B., and W. F. Shadwick. "The GS algorithm for exact linearization to Brunovsky normal form." IEEE Transactions on Automatic Control 37, no. 2 (1992): 224–30. http://dx.doi.org/10.1109/9.121623.

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6

Ghanooni, Pooria, Hamed Habibi, Amirmehdi Yazdani, Hai Wang, Somaiyeh MahmoudZadeh, and Amin Mahmoudi. "Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers." Electronics 10, no. 21 (2021): 2637. http://dx.doi.org/10.3390/electronics10212637.

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This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault-

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7

Zeng, Wei, and Cong Wang. "Learning from NN output feedback control of nonlinear systems in Brunovsky canonical form." Journal of Control Theory and Applications 11, no. 2 (2013): 156–64. http://dx.doi.org/10.1007/s11768-013-1124-0.

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8

Boulkroune, Abdesselem, Sarah Hamel, Farouk Zouari, Abdelkrim Boukabou, and Asier Ibeas. "Output-Feedback Controller Based Projective Lag-Synchronization of Uncertain Chaotic Systems in the Presence of Input Nonlinearities." Mathematical Problems in Engineering 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/8045803.

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This paper solves the problem of projective lag-synchronization based on output-feedback control for chaotic drive-response systems with input dead-zone and sector nonlinearities. This class of the drive-response systems is assumed in Brunovsky form but with unavailable states and unknown dynamics. To effectively deal with both dead-zone and sector nonlinearities, the proposed controller is designed in a variable-structure framework. To online learn the uncertain dynamics, adaptive fuzzy systems are used. And to estimate the unavailable states, a simple synchronization error is constructed. To

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9

Cong, Lanmei, Xiaocong Li, and Ancai Zhang. "Multiobject Holographic Feedback Control of Differential Algebraic System with Application to Power System." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/415281.

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A multiobject holographic feedback (MOHF) control method for studying the nonlinear differential algebraic (NDA) system is proposed. In this method, the nonlinear control law is designed in a homeomorphous linear space by means of constructing the multiobject equations (MOEq) which is in accord with Brunovsky normal form. The objective functions of MOEq are considered to be the errors between the output functions and their references. The relative degree for algebraic system is defined that is key to connecting the nonlinear and the linear control laws. Pole assignment method is addressed for

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10

RIGATOS, GERASIMOS, and EFTHYMIA RIGATOU. "SYNCHRONIZATION OF CIRCADIAN OSCILLATORS AND PROTEIN SYNTHESIS CONTROL USING THE DERIVATIVE-FREE NONLINEAR KALMAN FILTER." Journal of Biological Systems 22, no. 04 (2014): 631–57. http://dx.doi.org/10.1142/s0218339014500259.

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The paper proposes a new method for synchronization of coupled circadian cells and for nonlinear control of the associated protein synthesis process using differential flatness theory and the derivative-free nonlinear Kalman filter. By proving that the dynamic model of the FRQ protein synthesis is a differentially flat one, its transformation to the linear canonical (Brunovsky) form becomes possible. For the transformed model, one can find a state feedback control input that makes the oscillatory characteristics in the concentration of the FRQ protein vary according to desirable setpoints. To

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