Готові списки джерел за темами / Form Brunovsky

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Добірка наукової літератури з теми "Form Brunovsky"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Form Brunovsky"

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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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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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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Анотація:

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 properties. The existence and boundness of the control signal is always assured by introducing a novel method of parameter hopping and incorporating it in weight updating laws. Simulations illustrate the potency of the method and its applicability is tested on well known benchmarks, as well as in a bioreactor application. It is shown that the proposed approach is superior to the case of simple recurrent high order neural networks (HONN's).

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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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Анотація:

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 advantages of both V. I. Zubov’s method and P. Brunovsky’s method, and allows one to reduce the problem with multidimensional control to the problem of stabilizing a set of independent subsystems with scalar control for each subsystem.

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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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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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Анотація:

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- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

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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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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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Анотація:

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 prove the stability of the overall closed-loop system (controller, observer, and drive-response system) and to design the adaptation laws, a Lyapunov theory and strictly positive real (SPR) approach are exploited. Finally, three academic examples are given to show the effectiveness of this proposed lag-synchronization scheme.

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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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Анотація:

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 the stability domain of this MOHF control. Since there is no any approximation, the MOHF control is effective in governing the dynamic performance stably both to the small and major disturbance. The application in single machine infinite system (SMIS) shows that this approach is effective in the improvement of stable and transient stability for power system on the disturbance of active power or three-phase short circuit fault.

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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 estimate nonmeasurable elements of the state vector, the derivative-free nonlinear Kalman filter is used. The derivative-free nonlinear Kalman filter consists of the standard Kalman filter recursion on the linearized equivalent model of the coupled circadian cells and on computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. Moreover, to cope with parametric uncertainties in the model of the FRQ protein synthesis and with stochastic disturbances in measurements, the derivative-free nonlinear Kalman filter is redesigned in the form of a disturbance observer. The efficiency of the proposed Kalman filter-based control scheme is tested through simulation experiments.

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