• Title/Summary/Keyword: chaotic control

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Control of chaotic dynamics by magnetorheological damping of a pendulum vibration absorber

  • Kecik, Krzysztof
    • Structural Engineering and Mechanics
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    • v.51 no.5
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    • pp.743-754
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    • 2014
  • Investigations of regular and chaotic vibrations of the autoparametric pendulum absorber suspended on a nonlinear coil spring and a magnetorheological damper are presented in the paper. Application of a semi-active damper allows controlling the dangerous motion without stooping of system and additionally gives new possibilities for designers. The investigations are curried out close to the main parametric resonance. Obtained numerical and experimental results show that the semi-active suspension may reduce dangerous motion and it also allows to maintain the pendulum at a given attractor or to jump to another one. Moreover, the results show that, for some parameters, MR damping may transit to chaotic motions.

ON CONTROLLING A CHAOTIC VEHICLE DYNAMIC SYSTEM BY USING DITHER

  • Chang, S.C.
    • International Journal of Automotive Technology
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    • v.8 no.4
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    • pp.467-476
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    • 2007
  • This work verifies the chaotic motion of a steer-by-wire vehicle dynamic system, and then elucidates an application of dither smoothing to control the chaos of a vehicle model. The largest Lyapunov exponent is estimated from the synchronization to identify periodic and chaotic motions. Then, a bifurcation diagram reveals complex nonlinear behaviors over a range of parameter values. Finally, a method for controlling a chaotic vehicle dynamic system is proposed. This method involves applying another external input, called a dither signal, to the system. The designed controller is demonstrated to work quite well for nonlinear systems in achieving robust stability and protecting the vehicle from slip or spin. Some simulation results are presented to establish the feasibility of the proposed method.

The Synchronization Method for Cooperative Control of Chaotic UAV (카오스 소형 무인 비행체의 협조 제어를 위한 동기화 기법)

  • Bae, Young-Chul
    • Journal of Intelligence and Information Systems
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    • v.11 no.3
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    • pp.45-55
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    • 2005
  • In this paper, we propose a method to a synchronization of chaotic UAVs(Unmanned Aerial Vehicle) that have unstable limit cycles in a chaos trajectory surface. We assume all obstacles in the chaos trajectory surface have a Van der Pol equation with an unstable limit cycle. The proposed methods are assumed that if one of two chaotic UAVs receives the synchronization command, the other UAV also follows the same trajectory during the chaotic UAVs search on the arbitrary surface.

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DENSITY DEPENDENT MORTALITY OF INTERMEDIATE PREDATOR CONTROLS CHAOS-CONCLUSION DRAWN FROM A TRI-TROPHIC FOOD CHAIN

  • NATH, BINAYAK;DAS, KRISHNA PADA
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.22 no.3
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    • pp.179-199
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    • 2018
  • The paper explores a tri-trophic food chain model with density dependent mortality of intermediate predator. To analyze this aspect, we have worked out the local stability of different equilibrium points. We have also derived the conditions for global stability of interior equilibrium point and conditions for persistence of model system. To observe the global behaviour of the system, we performed extensive numerical simulations. Our simulation results reveal that chaotic dynamics is produced for increasing value of half-saturation constant. We have also observed trajectory motions around different equilibrium points. It is noticed that chaotic dynamics has been controlled by increasing value of density dependent mortality parameter. So, we conclude that the density dependent mortality parameter can be used to control chaotic dynamics. We also applied basic tools of nonlinear dynamics such as Poincare section and Lyapunov exponent to investigate chaotic behaviour of the system.

CONTROL OF LASER WELD KEYHOLE DYNAMICS BY POWER MODULATION

  • Cho, Min-Hyun;Dave Farson
    • Proceedings of the KWS Conference
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    • 2002.10a
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    • pp.600-605
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    • 2002
  • The keyhole formed by high energy density laser-material interaction periodically collapses due to surface tension of the molten metal in partial penetration welds. The collapse sometimes traps a void at the bottom of the keyhole, and it remains as welding defects. This phenomenon is seen as one cause of the instability of the keyhole during laser beam welding. Thus, it seems likely that improving the stability of the keyhole can reduce voids and uniform the penetration depth. The goal of this work is to develop techniques for controlling laser weld keyhole dynamics to reduce weld defects such as voids and inconsistent penetration. Statistical analysis of the penetration depth signals in glycerin determined that keyhole dynamics are chaotic. The chaotic nature of keyhole fluctuations and the ability of laser power modulation to control them have been demonstrated by high-speed video images of laser welds in glycerin. Additionally, an incident leading beam angle is applied to enhance the stability of the keyhole. The quasi-sinusoidal laser beam power of 400Hz frequency and 15$^{\circ}$ incident leading beam angle were determined to be the optimum parameters for the reduction of voids. Finally, chaos analyses of uncontrolled signals and controlled signals were done to show the effectiveness of modulation on the keyhole dynamics. Three-dimensional phase plots for uncontrolled system and controlled system are produced to demonstrate that the chaotic keyhole dynamics is converted to regular periodic behavior by control methods: power modulation and incident leading beam angle.

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Model Predictive Control of Discrete-Time Chaotic Systems Using Neural Network (신경회로망을 이용한 이산치 혼돈 시스템의 모델 예측제어)

  • Kim, Se-Min;Choi, Yoon-Ho;Park, Jin-Bae;Joo, Young-Hoon
    • Proceedings of the KIEE Conference
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    • 1999.07b
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    • pp.933-935
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    • 1999
  • In this paper, we present model predictive control scheme based on neural network to control discrete-time chaotic systems. We use a feedforward neural network as nonlinear prediction model. The training algorithm used is an adaptive backpropagation algorithm that tunes the connection weights. And control signal is obtained by using gradient descent (GD), some kind of LMS method. We identify that the system identification results through model prediction control have a great effect on control performance. Finally, simulation results show that the proposed control algorithm performs much better than the conventional controller.

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Dynamical Rolling Analysis of a Vessel in Regular Beam Seas

  • Lee, Sang-Do;You, Sam-Sang
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.24 no.3
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    • pp.325-331
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    • 2018
  • This paper deals with the dynamical analysis of a vessel that leads to capsize in regular beam seas. The complete investigation of nonlinear behaviors includes sub-harmonic motion, bifurcation, and chaos under variations of control parameters. The vessel rolling motions can exhibit various undesirable nonlinear phenomena. We have employed a linear-plus-cubic type damping term (LPCD) in a nonlinear rolling equation. Using the fourth order Runge-Kutta algorithm with the phase portraits, various dynamical behaviors (limit cycles, bifurcations, and chaos) are presented in beam seas. On increasing the value of control parameter ${\Omega}$, chaotic behavior interspersed with intermittent periodic windows are clearly observed in the numerical simulations. The chaotic region is widely spread according to system parameter ${\Omega}$ in the range of 0.1 to 0.9. When the value of the control parameter is increased beyond the chaotic region, periodic solutions are dominant in the range of frequency ratio ${\Omega}=1.01{\sim}1.6$. In addition, one more important feature is that different types of stable harmonic motions such as periodicity of 2T, 3T, 4T and 5T exist in the range of ${\Omega}=0.34{\sim}0.83$.

Control of Chaos Dynamics in Jordan Recurrent Neural Networks

  • Jin, Sang-Ho;Kenichi, Abe
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.43.1-43
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    • 2001
  • We propose two control methods of the Lyapunov exponents for Jordan-type recurrent neural networks. Both the two methods are formulated by a gradient-based learning method. The first method is derived strictly from the definition of the Lyapunov exponents that are represented by the state transition of the recurrent networks. The first method can control the complete set of the exponents, called the Lyapunov spectrum, however, it is computationally expensive because of its inherent recursive way to calculate the changes of the network parameters. Also this recursive calculation causes an unstable control when, at least, one of the exponents is positive, such as the largest Lyapunov exponent in the recurrent networks with chaotic dynamics. To improve stability in the chaotic situation, we propose a non recursive formulation by approximating ...

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Prediction of Chaotic Time Series Using Fuzzy Identification (퍼지 식별을 이용한 카오스 시계열 데이터 예측)

  • Ko, Jae-Ho;Bang, Sung-Yun;Do, Byung-Jo;Bae, Young-Chul;Yim, Hwa-Yeoung
    • Proceedings of the KIEE Conference
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    • 1997.07b
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    • pp.627-629
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    • 1997
  • In this paper, fuzzy logic system equipped with the back-propagation training algorithm as identifiers for nonlinear dynamic systems is described. To improve its performance, Jacob's delta-bar -delta rule is adapted in adjusting stepsize ${\alpha}$, and only y and ${\alpha}$ updating algorithm is suggested. In identifying and predicting the chaotic time series, suggested method is better than Li-Xin Wang's method,[1]

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A Study on High Impedance Fault Defection Method Using Neural Nets and Chaotic Phenoma (신경망과 카오스 현상을 이용한 고저항 지락 사고 검출 기법에 관한 연구)

  • Ryu, Chang-Wan;Shim, Jae-Chul;Ko, Jae-Ho;Bae, Young-Chul;Yim, Wha-Yeong
    • Proceedings of the KIEE Conference
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    • 1997.07c
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    • pp.897-899
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    • 1997
  • The analysis of distribution line faults is essential to the proper protections of the power system. A high impedance fault does not make enough current to cause conventional protective devices. It is well known that undesirable operating conditions and certain types of faults on electric distribution feeders cannot be detected by using conventional protection system. This paper describes an algorithm using back-propagation neural network for pattern recognition and detection of high impedance faults. Fractal dimensions are estimated for distinction between random noise and chaotic behavior in the power system. The fractal dimension of the line current is also used as a indication of the high impedance fault.

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