• Title, Summary, Keyword: Volterra kernel

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A New Method for Identifying Higher Volterra Kernel Having the Same Time Coordinate for Nonlinear System

  • Nishiyama, Eiji;Harada, Hiroshi;Rong, Li;Kashiwagi, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.137-140
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    • 1999
  • A lot of researcher have proposed a method of kernel identifying nonlinear system by use of Wiener kernels[6-7] or Volterra kernel[5] and so on. In this research, the authors proposed a method of identifying Volterra kernels for nonlinear system by use of pseudorandom M-sequence in which a crosscorrelation function between input and output of a nonlinear system is taken[4]. we can be applied to an MISO nonlinear system or a system which depends on its input amplitude[2]. But, there exist many systems in which it is difficult to determine a Volterra kernel having the same time coordinate on the crosscorrelation function. In those cases, we have to estimate Volterra kernel by using its neighboring points[4]. In this paper, we propose a new method for not estimating but obtaining Volterra kernel having the same time coordinate using calculation between the neighboring points. Some numerical simulations show that this method is effective for obtaining higher order Volterra kernel of nonlinear control systems.

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Identification of Volterra Kernels of Nonlinear System Having Backlash Type Nonlinearity

  • Rong, Li;Kashiwagi, H.;Harada, H.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.141-144
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    • 1999
  • The authors have recently developed a new method for identification of Volterra kernels of nonlinear systems by use of pseudorandom M-sequence and correlation technique. And it is shown that nonlinear systems which can be expressed by Volterra series expansion are well identified by use of this method. However, there exist many nonlinear systems which can not be expressed by Volterra series mathematically. A nonlinear system having backlash type nonliear element is one of those systems, since backlash type nonlinear element has multi-valued function between its input and output. Since Volterra kernel expression of nonlinear system is one of the most useful representations of non-linear dynamical systems, it is of interest how the method of Volterra kernel identification can be ar plied to such backlash type nonlinear system. The authors have investigated the effect of application of Volterra kernel identification to those non-linear systems which, accurately speaking, is difficult to express by use of Volterra kernel expression. A pseudorandom M-sequence is applied to a nonlinear backlash-type system, and the crosscorrelation function is measured and Volterra kernels are obtained. The comparison of actual output and the estimated output by use of measured Volterra kernels show that we can still use Volterra kernel representation for those backlash-type nonlinear systems.

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Identification of Volterra Kernels of Nonlinear Van do Vusse Reactor

  • Kashiwagi, Hiroshi;Rong, Li
    • Transactions on Control, Automation and Systems Engineering
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    • v.4 no.2
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    • pp.109-113
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    • 2002
  • Van de Vusse reactor is known as a highly nonlinear chemical process and has been considered by a number of researchers as a benchmark problem for nonlinear chemical process. Various identification methods for nonlinear system are also verified by applying these methods to Van de Vusse reactor. From the point of view of identification, only the Volterra kernel of second order has been obtained until now. In this paper, the authors show that Volterra kernels of nonlinear Van de Vusse reactor of up to 3rd order are obtained by use of M-sequence correlation method. A pseudo-random M-sequence is applied to Van de Vusse reactor as an input and its output is measured. Taking the crosscorrelation function between the input and the output, we obtain up to 3rd order Volterra kernels, which is the highest order Volterra kernel obtained until now for Van de Vusse reactor. Computer simulations show that when Van de Vusse chemical process is identified by use of up to 3rd order Volterra kernels, a good agreement is observed between the calculated output and the actual output.

MODEL PREDICTIVE CONTROL OF NONLINEAR PROCESSES BY USE OF 2ND AND 3RD VOLTERRA KERNEL MODEL

  • Kashiwagi, H.;Rong, L.;Harada, H.;Yamaguchi, T.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.451-454
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    • 1998
  • This paper proposes a new method of Model Predictive Control (MPC) of nonlinear process by us-ing the measured Volterra kernels as the nonlinear model. A nonlinear dynamical process is usually de-scribed as Volterra kernel representation, In the authors' method, a pseudo-random M-sequence is ar plied to the nonlinear process, and its output is measured. Taking the crosscorrelation between the input and output, we obtain the Volterra kernels up to 3rd order which represent the nonlinear characteristics of the process. By using the measured Volterra kernels, we can construct the nonlinear model for MPC. In applying Model Predictive Control to a nonlinear process, the most important thing is, in general, what kind of nonlinear model should be used. The authors used the measured Volterra kernels of up to 3rd order as the process model. The authors have carried out computer simulations and compared the simulation results for the linear model, the nonlinear model up to 2nd Volterra kernel, and the nonlinear model up to 3rd order Vol-terra kernel. The results of computer simulation show that the use of Valterra kernels of up to 3rd order is most effective for Model Predictive Control of nonlinear dynamical processes.

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Adaptive identification of volterra kernel of nonlinear systems

  • Yeping, Sun;Kashiwagi, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.476-479
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    • 1995
  • A real time and adaptive method for obtaining Volterra kernels of a nonlinear system by use of pseudorandom M-sequences and correlation technique is proposed. The Volterra kernels are calculated real time and the obtained Volterra kernels becomes more accurate as time goes on. The simulation results show the effectiveness of this method for identifying time-varying nonlinear system.

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Identification of Polymerization Reactor Using Third Order Volterra Kernel Model

  • Numata, Motoki;Kashiwagi, Hiroshi;Harada, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.26.2-26
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    • 2001
  • It is known that Volterra kernel model can represent a wide variety of nonlinear chemical processes. But almost all Volterra kernel models which appeared in the literature are up to second order, because it was difficult to measure higher order Volterra kernels. Kashiwagi has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. In this paper, the authors verified the applicability of this method for chemical processes using polymerization reactor simulation. Also, the authors have recently proposed a practical Identification method for chemical processes, which is based on the combination of off-line nonlinear identification and on-line linear identification. This method is also applied to the identification of polymerization reactor, and we obtained ...

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A Practical Method for Identification of Nonlinear Chemical Processes by use of Volterra Kernel Model

  • Numata, Motoki;Kashiwagi, Hiroshi;Harada, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.145-148
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    • 1999
  • It is known that Volterra kernel models can represent a wide variety of nonlinear chemical processes. Also, it is necessary for Volterra model identification to excite the process to be identified with a signal having wide range of frequency spectrum and high enough amplitude of input signals. Kashiwagi[4 ∼ 7] has recently shown a method for measuring Volterra kernels up to third order using pseudorandom M-sequence signals. However, in practice, since it is not always possible to apply such input sequences to the actual chemical plants. Even when we can apply such a pseudorandom signal to the process, it takes much time to obtain higher order Volterra kernels. Considering these problems, the authors propose here a new method for practical identification of Volterra kernels by use of approximate open differential equation (ODE) model and simple plant test. Simulation results are shown for verifying the usefulness of our method of identification of nonlinear chemical processes.

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ON A DISCUSSION OF NONLINEAR INTEGRAL EQUATION OF TYPE VOLTERRA-HAMMERSTEIN

  • El-Borai, M.M.;Abdou, M.A.;El-Kojok, M.M.
    • The Pure and Applied Mathematics
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    • v.15 no.1
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    • pp.1-17
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    • 2008
  • Here, we consider the existence and uniqueness solution of nonlinear integral equation of the second kind of type Volterra-Hammerstein. Also, the normality and continuity of the integral operator are discussed. A numerical method is used to obtain a system of nonlinear integral equations in position. The solution is obtained, and many applications in one, two and three dimensionals are considered.

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Identification of Backlash Nonlinear System by use of M-sequence and correlation

  • Kashiwagi, H.;Rong, Li.;Harada, H.
    • 제어로봇시스템학회:학술대회논문집
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    • pp.470-470
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    • 2000
  • This paper describes a new method of identifcation of backlash nonlinear systems by use of M-sequence correlation method. In this method, we can obtain not only Volterra kernels of up to 3rd order of the nonlinear system, but also the width of the backlash element from observing the crosscorrelation between the input and the output. Here strictly speaking, a multi-valued nonlinear system such as backlash element can not be expressed by Volterra kernel representation mathematically. But in practice, we encounter many cases where it is difficult to treat them mathematically but they can be controlled from experience. So we here dare to suppose that backlash nonlinear system can be approximated by Volterra kernel representation. Simulations are carried out on a nonlinear system consisting of linear part plus backlash element. And Volterra kernels are measured. The output calculated from the observed Volterra kernels is in good agreement wi th the actual output. And we show that we can obtain the width of backlash element, which is one of the most important parameters, by observing the maximum value of crosscorrelation function between the input M-sequence and the output.

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Linearization of nonlinear system by use of volterra kernel

  • Nishiyama, Eiji;Kashiwagi, Hiroshi
    • 제어로봇시스템학회:학술대회논문집
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    • pp.149-152
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    • 1996
  • In this paper, the authors propose a new method for linearizing a nonlinear dynamical system by use of Volterra kernel of the nonlinear system. The authors have recently obtained a new method for measuring Volterra kernels of nonlinear control systems by use of a pseudo-random M-sequence and correlation technique. In this method, an M-sequence is applied to the nonlinear system and the crosscorrelation function between the input and the output gives us every crosssection of Volterra kernels up to 3rd order. Once we can get Volterra kernels of nonlinear system, we can construct a linearization method of the nonlinear system. Simulation results show good agreement between the observed results and the theoretical considerations.

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