• Title, Summary, Keyword: Nonlinear systems

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

  • 강정규;김정수;김성호
    • 제어로봇시스템학회:학술대회논문집
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    • pp.236-236
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    • 2000
  • 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 systems is their robustness with respect to a model mismatch and disturbances. But it was difficult to apply for nonlinear systems. Adaptive Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to identify a nonlinear dynamical systems. In this paper, we propose new IMC design method using adaptive neuro-fuzzy inference system for nonlinear plant. Numerical simulation results show that proposed IMC design method has good performance than classical PID controller.

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Receding Horizon Predictive Control for Nonlinear Time-delay Systems

  • Kwon, Wook-Hyun;Lee, Young-Sam;Han, Soo-Hee
    • 제어로봇시스템학회:학술대회논문집
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    • pp.27.2-27
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    • 2001
  • This paper proposes a receding horizon predictive control (RHPC) for nonlinear time-delay systems. The control law is obtained by minimizing finite horizon cost with a terminal weighting functional. An inequality condition on the terminal weighting functional is presented, under which the closed-loop stability of RHPC is guaranteed, A special class of nonlinear time-delay systems is introduced and a systematic method to find a terminal weighting functional satisfying the proposed inequality condition is given for these systems. Through a simulation example, it is demonstrated that the proposed RHPC has the guaranteed closed-loop stability for nonlinear time-delay systems.

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Switching Control for Second Order Nonlinear Systems Using Singular Hyperplanes

  • Yeom Dong-Hae;Choi Jin-Young
    • International Journal of Control, Automation, and Systems
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    • v.4 no.1
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    • pp.124-135
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    • 2006
  • In this paper, we propose a switching control method for a class of 2nd order nonlinear systems with single input. The main idea is to switch the control law before the trajectory of the solution arrives at singular hyperplanes which are defined by the denominator of the control law. The proposed method can handle a class of nonlinear systems which is difficult to be stabilized by the existing methods such as feedback linearization, backstepping, control Lyapunov function, and sliding mode control.

Robust stabilization of nonlinear uncertain systems without matching conditions (정합조건을 만족하지 않는 불확정 비선형 시스템의 강인 안정화)

  • 주진만;최윤호;박진배
    • 제어로봇시스템학회:학술대회논문집
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    • pp.159-162
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    • 1997
  • This paper describes robust stabilization of nonlinear single-input uncertain systems without matching conditions. We consider nonlinear systems with a vector of unknown constant parameters perturbed about a known value. The approach utilizes the generalized controller canonical form to lump the unmatched uncertainties recursively into the matched ones. This can be achieved via nonlinear coordinate transformations which depend not only on the states of the nonlinear system but also on the control input. Then the dynamic robust control law is derived and the stability result is also presented.

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Design of Generalized Minimum Variance Controllers for Nonlinear Systems

  • Grimble Michael J.
    • International Journal of Control, Automation, and Systems
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    • v.4 no.3
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    • pp.281-292
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    • 2006
  • The design and implementation of Generalized Minimum Variance control laws for nonlinear multivariable systems that can include severe nonlinearities is considered. The quadratic cost index minimised involves dynamically weighted error and nonlinear control signal costing terms. The aim here is to show the controller obtained is simple to design and implement. The features of the control law are explored. The controller obtained includes an internal model of the process and in one form is a nonlinear version of the Smith Predictor.

Identification of nonlinear dynamical systems based on self-organized distributed networks (자율분산 신경망을 이용한 비선형 동적 시스템 식별)

  • 최종수;김형석;김성중;권오신;김종만
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.45 no.4
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    • pp.574-581
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    • 1996
  • The neural network approach has been shown to be a general scheme for nonlinear dynamical system identification. Unfortunately the error surface of a Multilayer Neural Networks(MNN) that widely used is often highly complex. This is a disadvantage and potential traps may exist in the identification procedure. The objective of this paper is to identify a nonlinear dynamical systems based on Self-Organized Distributed Networks (SODN). The learning with the SODN is fast and precise. Such properties are caused from the local learning mechanism. Each local network learns only data in a subregion. This paper also discusses neural network as identifier of nonlinear dynamical systems. The structure of nonlinear system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems. (author). 13 refs., 7 figs., 2 tabs.

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An Observer for Nonlinear Systems Using Approximate Observer Form (근사 관측기 형태를 이용한 비선형 시스템의 관측기)

  • Lee, Sungryul;Sin, Hyeon-Seok;Park, Mignon
    • Journal of Institute of Control, Robotics and Systems
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    • v.7 no.6
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    • pp.471-476
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    • 2001
  • This paper presents a state observer for nonlinear systems using approximate observer from. It is shown that if a nonlinear system is approximately error linearizable, then there exists a local nonlinear observer whose estimation error converges exponentially to zero. Since the proposed method relaxes strong geometric conditions of previous works, it improves the existing results for nonlinear observer design. Finally, some example is given to show the effectiveness of this scheme.

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A Novel Stabilizing Control for Neural Nonlinear Systems with Time Delays by State and Dynamic Output Feedback

  • Liu, Mei-Qin;Wang, Hui-Fang
    • International Journal of Control, Automation, and Systems
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    • v.6 no.1
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    • pp.24-34
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    • 2008
  • A novel neural network model, termed the standard neural network model (SNNM), similar to the nominal model in linear robust control theory, is suggested to facilitate the synthesis of controllers for delayed (or non-delayed) nonlinear systems composed of neural networks. The model is composed of a linear dynamic system and a bounded static delayed (or non-delayed) nonlinear operator. Based on the global asymptotic stability analysis of SNNMs, Static state-feedback controller and dynamic output feedback controller are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based nonlinear systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Two application examples are given where the SNNMs are employed to synthesize the feedback stabilizing controllers for an SISO nonlinear system modeled by the neural network, and for a chaotic neural network, respectively. Through these examples, it is demonstrated that the SNNM not only makes controller synthesis of neural-network-based systems much easier, but also provides a new approach to the synthesis of the controllers for the other type of nonlinear systems.

Identification of volterra kernal of nonlinear systems by use of M-sequence

  • Kashiwagi, Hiroshi;Yeping, Sun;Nishiyama, Eiji
    • 제어로봇시스템학회:학술대회논문집
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    • pp.150-154
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    • 1993
  • A new method is proposed for obtaining Volterra kernals of a nonlinear system by use of a nonlinear systems by use of pseudorandom M-sequences and correlation technique. M-sequence is applied to a nonlinear technique. M-sequence is applied to a nonlinear system and the crosscorrelation function between the input and the output displays not only the linear impulse response of the linear part of the system, but also crosssections of the Volterra kernals of nonlinear system. Simulations are carried out for up to 3rd order Volterra kernal, and the results show a good agreement with the theoretical considerations.

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