• Title, Summary, Keyword: Nonlinear systems

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Time Domain Identification of Nonlinear Structural Dynamic Systems Using Unscented Kalman Filter (Unscented Kalman Filter를 이용한 비선형 동적 구조계의 시간영역 규명기법)

  • Yun, Chung-Bang;Koo, Ki-Young
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • pp.117-126
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    • 2001
  • In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

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Adaptive Neural Dynamic Surface Control via H Approach for Nonlinear Flight Systems (비선형 비행 시스템을 위한 H 접근법 기반 적응 신경망 동적 표면 제어)

  • Yoo, Sung-Jin;Choi, Yoon-Ho
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.3
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    • pp.254-262
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    • 2008
  • In this paper, we propose an adaptive neural dynamic surface control (DSC) approach with $H_{\infty}$ tracking performance for full dynamics of nonlinear flight systems. It is assumed that the model uncertainties such as structured and unstrutured uncertainties, and external disturbances influence the nonlinear aircraft model. In our control system, self recurrent wavelet neural networks (SRWNNs) are used to compensate the model uncertainties of nonlinear flight systems, and an adaptive DSC technique is extended for the disturbance attenuation of nonlinear flight systems. All weights of SRWNNs are trained on-line by the smooth projection algorithm. From Lyapunov stability theorem, it is shown that $H_{\infty}$ performance nom external disturbances can be obtained. Finally, we present the simulation results for a nonlinear six-degree-of-freedom F-16 aircraft model to confirm the effectiveness of the proposed control system.

A pole assignment control design for single-input double-output nonlinear mechanical systems

  • Kobayashi, Masahito;Tamura, Katsutoshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.144-149
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    • 1993
  • This paper discusses a design of a nonlinear control for a class of single-input double-output nonlinear mechanical systems. When conventional linearization methods are applied to the mechanical systems, some problems of oscillation and unstable phenomena arise. The proposed nonlinear control system resolves these problems. In this design the eigenvalues of the closed-loop nonlinear system are assigned to desired locations and local asymptotic stability of the closed-loop system. is guaranteed. The design method is applied to an inverted pendulum system with a moving weight mechanism. Experimental results show that the proposed nonlinear controller is more effective for stability than the usual linear controller.

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Neural Model Predictive Control for Nonlinear Chemical Processes

  • Song, Jeong-Jun;Park, Sunwon
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.899-902
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    • 1993
  • A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (MNPC) shows good performances and robustness. To whom all correspondence should be addressed.

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LYAPUNOV FUNCTIONS FOR NONLINEAR DIFFERENCE EQUATIONS

  • Choi, Sung Kyu;Cui, Yinhua;Koo, Namjip
    • Journal of the Chungcheong Mathematical Society
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    • v.24 no.4
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    • pp.883-893
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    • 2011
  • In this paper we study h-stability of the solutions of nonlinear difference system via the notion of $n_{\infty}$-summable similarity between its variational systems. Also, we show that two concepts of h-stability and h-stability in variation for nonlinear difference systems are equivalent. Furthermore, we characterize h-stability for nonlinear difference systems by using Lyapunov functions.

A State Observer of Nonlinear Systems with Delayed Output (지연된 출력을 갖는 비선형 시스템의 상태 관측기)

  • Lee, Sung-Ryul
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.7
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    • pp.613-616
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    • 2012
  • This paper proposes the state observer design for nonlinear systems with delayed output. It is shown that by considering a nonlinear term of error dynamics as an additional state variable, the nonlinear error dynamics with time delay can be transformed into the linear one with time delay. Sufficient conditions for existence of a state observer are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

Design of IMC for Nonlinear Systems by Using Adaptive Neuro-Fuzzy Inference System (뉴로 퍼지 시스템을 이용한 비선형 시스템의 IMC 제어기 설계)

  • Kim, Sung-Ho;Kang, Jung-Kyu
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.11
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    • pp.958-961
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    • 2001
  • Control of Industrial processes is very difficult due to nonlinear dynamics, effect of disturbances and modeling errors. M.Morari proposed Internal Model Control(IMC) system that can be effectively applied to the systems with model uncertainties and time delays. The advantage of IMC is their robustness with respect to a model mismatch and disturbances. But it is difficult to apply for nonlinear systems. ANFIS(Adaptive Neuro-Fuzzy Inference System) which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in ANFIS can be effectively utilized to control a nonlinear systems. In this paper, we propose new ANFIS-based IMC controller for nonlinear systems. Numerical simulation results show that the proposed control scheme has good performances.

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Robust Nonlinear H$\infty$ FIR Filtering for Time-Varying Systems

  • Ryu, Hee-Seob;Son, Won-Kee;Kwon, Oh-Kyu
    • Transactions on Control, Automation and Systems Engineering
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    • v.2 no.3
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    • pp.175-181
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    • 2000
  • This paper investigates the robust nonlinear H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for nonlinear discrete time-varying uncertain systems represented by the state-space model having parameter uncertainty. Firstly, when there is no parameter uncertainty in the system, the discrete-time nominal nonlinear H$_{\infty}$ FIR filter is derived by using the equivalence relationship between the FIR filter and the recursive filter, which corresponds to the standard nonlinear H$_{\infty}$ filter. Secondly, when the system has the parameter uncertainty, the robust nonlinear H$_{\infty}$ FIR filter is proposed for the discrete-time nonlinear uncertain systems.

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Control of Discrete Time Nonlinear Systems with Input Delay (입력지연을 갖는 이산 시간 비선형 시스템의 제어)

  • Lee, Sung-Ryul
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.6
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    • pp.509-512
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    • 2012
  • This paper presents the state feedback control design for discrete time nonlinear systems where there exists a time delay in input. It is shown that under some boundedness condition, the time delay nonlinear systems can be transformed into the time delay linear systems with time varying parameters. Sufficient conditions for existence of stabilizing state feedback controller are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

A Method for Separating Volterra Kernels of Nonlinear Systems by Use of Different Amplitude M-sequences

  • Harada, Hiroshi;Nishiyama, Eiji;Kashiwagi, Hiroshi;Yamaguchi, Teruo
    • 제어로봇시스템학회:학술대회논문집
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    • pp.271-274
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    • 1998
  • This paper describes a new method for separation of the Volterra kernels which are identified by use of M-sequence. One of the authors has proposed a method for identification of Volterra kernels of nonlinear systems using M-sequence and correlation technique. When M-sequence are applied to a nonlinear systems, the cross-correlation function between the input and the output of the nonlinear systems includes cross-sections of high-order Volterra kernels. However, if various order Volterra kernels exixt on the obtained cross-correlation function, it is difficult to separate the Volterra kernels. In this paper, the authors show that the magnitude of Volterra kernels is maginified by the amplitude of M-sequence according to the order of Volterra kernels. By use of this property, each order Volterra kernels is obtained by solving linear equations. Simulations are carried out for some nonlinear systems. The results show that Volterra kernels can be separated in each order successfully by the proposed method.

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