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Q Yang - One of the best experts on this subject based on the ideXlab platform.

  • Linearization of Stack Piezoelectric Ceramic Actuators Based on BoucWen Model
    2016
    Co-Authors: Q Yang
    Abstract:

    ABSTRACT: In this article, in order to linearize the hysteresis behavior of stack piezoelectric ceramic actuators (SPCAs), the feedforward Linearization Method based on the BoucWen model and the hybrid Linearization Method combining the feedforward Method and PI feed-back loop are proposed and explored. The rapid control prototypes of the Linearization con-trollers for the SPCA are established and tested. The research results show that both the feedforward and hybrid Linearization Methods can linearize the hysteresis behavior and the SPCAs with Linearization controllers can be regarded as linear actuators. However, the line-arization accuracy using the feedforward Method is confined by the modeling error of the BoucWen model. Although the proposed hybrid Method combining the feedforward Method and the PI feedback loop can reach higher Linearization accuracy than that with the feedfor-ward Method, the hybrid Method will result in high frequency components in the additional voltage, which will have a serious impact on the lifespan of the controlled SPCA. Utilizing the Linearization Methods proposed in this article, the open-loop and closed-loop controls for the tip displacement of a piezoelectric-driven microgripper are realized, which indicates that the proposed Linearization Methods can simplify the control of SPCAs and SPCA-based sys-tems with high accuracy. Key Words: stack piezoelectric ceramic actuator, Linearization, feedforward compensation, hybrid control, PI control, BoucWen model

  • Linearization of stack piezoelectric ceramic actuators based on bouc wen model
    Journal of Intelligent Material Systems and Structures, 2011
    Co-Authors: D H Wang, W Zhu, Q Yang
    Abstract:

    In this article, in order to linearize the hysteresis behavior of stack piezoelectric ceramic actuators (SPCAs), the feedforward Linearization Method based on the Bouc-Wen model and the hybrid Linearization Method combining the feedforward Method and PI feedback loop are proposed and explored. The rapid control prototypes of the Linearization controllers for the SPCA are established and tested. The research results show that both the feedforward and hybrid Linearization Methods can linearize the hysteresis behavior and the SPCAs with Linearization controllers can be regarded as linear actuators. However, the Linearization accuracy using the feedforward Method is confined by the modeling error of the Bouc-Wen model. Although the proposed hybrid Method combining the feedforward Method and the PI feedback loop can reach higher Linearization accuracy than that with the feedforward Method, the hybrid Method will result in high frequency components in the additional voltage, which will have a serious impact on ...

J. C. Jimenez - One of the best experts on this subject based on the ideXlab platform.

  • multiple shooting local Linearization Method for the identification of dynamical systems
    Communications in Nonlinear Science and Numerical Simulation, 2016
    Co-Authors: Felix Carbonell, Yasser Iturriamedina, J. C. Jimenez
    Abstract:

    Abstract The combination of the multiple shooting strategy with the generalized Gauss–Newton algorithm turns out in a recognized Method for estimating parameters in ordinary differential equations (ODEs) from noisy discrete observations. A key issue for an efficient implementation of this Method is the accurate integration of the ODE and the evaluation of the derivatives involved in the optimization algorithm. In this paper, we study the feasibility of the Local Linearization (LL) approach for the simultaneous numerical integration of the ODE and the evaluation of such derivatives. This integration approach results in a stable Method for the accurate approximation of the derivatives with no more computational cost than that involved in the integration of the ODE. The numerical simulations show that the proposed Multiple Shooting-Local Linearization Method recovers the true parameters value under different scenarios of noisy data.

  • a higher order local Linearization Method for solving ordinary differential equations
    Applied Mathematics and Computation, 2007
    Co-Authors: H. Cruz, Rolando J Biscay, Felix Carbonell, Tohru Ozaki, J. C. Jimenez
    Abstract:

    The local Linearization (LL) Method for the integration of ordinary differential equations is an explicit one-step Method that has a number of suitable dynamical properties. However, a major drawback of the LL integrator is that its order of convergence is only two. The present paper overcomes this limitation by introducing a new class of numerical integrators, called the LLT Method, that is based on the addition of a correction term to the LL approximation. In this way an arbitrary order of convergence can be achieved while retaining the dynamic properties of the LL Method. In particular, it is proved that the LLT Method reproduces correctly the phase portrait of a dynamical system near hyperbolic stationary points to the order of convergence. The performance of the introduced Method is further illustrated through computer simulations.

  • local Linearization Method for numerical integration of delay differential equations
    SIAM Journal on Numerical Analysis, 2006
    Co-Authors: J. C. Jimenez, L M Pedroso, Felix Carbonell, V. Hernandez
    Abstract:

    In this paper, a new approach for the numerical computation of delay differential equations (DDEs) is introduced. The essential idea consists of obtaining numerical integrators that use a code expressly developed for linear DDEs, in contrast with the conventional approach of using a code for ordinary differential equations. Specifically, two numerical schemes of this new class of integrators are proposed and their numerical viability analyzed. It includes the estimation of the convergence rate, the evaluation of the computational cost of the schemes, and a simulation study. It is proved that these one-step explicit integrators converge uniformly with order two to the solution of nonlinear DDEs and are able to integrate stiff equations in a satisfactory way with low computational cost.

  • approximation of continuous time stochastic processes by the local Linearization Method revisited
    Stochastic Analysis and Applications, 2002
    Co-Authors: J. C. Jimenez, R. Biscay
    Abstract:

    This paper studies the order of uniform strong convergence of two Local Linear (LL) approximations to the solution of stochastic differential equations (SDEs) with additive noise. The results obtained cover multi-dimensional and non-autonomous SDEs, and also ordinary differential equations with random initial conditions. It is demonstrated that the global order of convergence of one of the LL approximations considered is actually larger than that reported in an earlier paper, so solving an apparent discrepancy between theory and recent simulation studies.

  • dynamic properties of the local Linearization Method for initial value problems
    Applied Mathematics and Computation, 2002
    Co-Authors: J. C. Jimenez, Rolando J Biscay, C Mora, L M Rodriguez
    Abstract:

    Some dynamic properties of the local Linearization (LL) scheme for the numerical integration of initial-value problems in ordinary differential equations (ODEs) are investigated. Specifically, the general conditions under which this scheme preserves the stationary points and periodic orbits of the ODEs and the local stability at these steady states are studied. These dynamic properties are also examined by means of numerical experiments and the results are compared with those achieved by other numerical schemes. In addition, a brief review of the computational implementations of the LL scheme is also presented.

Isaac Elishakoff - One of the best experts on this subject based on the ideXlab platform.

  • a novel local stochastic Linearization Method via two extremum entropy principles
    International Journal of Non-linear Mechanics, 2002
    Co-Authors: Giuseppe Ricciardi, Isaac Elishakoff
    Abstract:

    The classical Gaussian stochastic Linearization Method for non-linear random vibration problems is reinterpreted on the basis of the maximum entropy principle. Starting from this theoretical result, the maximum entropy principle allows to formulate a local stochastic Linearization Method, based on the substitution of the original non-linear system by an equivalent locally linear one. The expressions of the equivalent coefficients are derived. The equivalence of this Method with a non-Gaussian closure based on the maximum entropy Method for stochastic dynamics is evidenced. In addition, an alternative stochastic Linearization Method is proposed, based on the minimum cross-entropy principle. Numerical applications show the superiority of the two proposed local stochastic Linearization Methods over the Gaussian one.

  • nonlinear response of a beam under stationary random excitation by improved stochastic Linearization Method
    Applied Mathematical Modelling, 1995
    Co-Authors: Jianjie Fang, Isaac Elishakoff, Raoul Caimi
    Abstract:

    Abstract The new stochastic Linearization technique is employed to investigate the nonlinear mean square response of a beam with artitrary boundary conditions under time-dependent stationary random excitation. To demonstrate the accuracy of this new Method, mean square response is obtained through both the new and conventional versions of the stochastic Linearization technique. An example of a beam with both ends simply supported is presented for the case of white noise excitation to illustrate the present Method. Numerical simulations are performed to check the accuracy of the modified technique. It is shown that the modified version can yield more accurate results for the mean square response of the beam than the conventional stochastic Linearization technique, especially when there exists a large nonlinearity.

D H Wang - One of the best experts on this subject based on the ideXlab platform.

  • Linearization of stack piezoelectric ceramic actuators based on bouc wen model
    Journal of Intelligent Material Systems and Structures, 2011
    Co-Authors: D H Wang, W Zhu, Q Yang
    Abstract:

    In this article, in order to linearize the hysteresis behavior of stack piezoelectric ceramic actuators (SPCAs), the feedforward Linearization Method based on the Bouc-Wen model and the hybrid Linearization Method combining the feedforward Method and PI feedback loop are proposed and explored. The rapid control prototypes of the Linearization controllers for the SPCA are established and tested. The research results show that both the feedforward and hybrid Linearization Methods can linearize the hysteresis behavior and the SPCAs with Linearization controllers can be regarded as linear actuators. However, the Linearization accuracy using the feedforward Method is confined by the modeling error of the Bouc-Wen model. Although the proposed hybrid Method combining the feedforward Method and the PI feedback loop can reach higher Linearization accuracy than that with the feedforward Method, the hybrid Method will result in high frequency components in the additional voltage, which will have a serious impact on ...

Armen Der Kiureghian - One of the best experts on this subject based on the ideXlab platform.

  • nonlinear stochastic dynamic analysis by evolutionary tail equivalent Linearization Method
    Structural Safety, 2021
    Co-Authors: Marco Broccardo, Armen Der Kiureghian
    Abstract:

    Abstract This study introduces the evolutionary tail-equivalent Linearization Method (ETELM) for nonlinear stochastic dynamic analysis. The Method builds on the recently developed tail-equivalent Linearization Method (TELM) and it is designed for the class of evolutionary processes. The original TELM employs a tail-equivalent linear system (TELS) by equating the tail probability of a linear system response for a specified threshold to the first-order approximation of the tail probability of the nonlinear system response. For stationary problems, the TELS is time-independent and only one linear system needs to be defined for the specified threshold. However, for a transient input, the TELS is time dependent and an evolutionary tail-equivalent linear system (ETELS) must be defined to study the entire transient response. Algorithms are developed to determine a discrete-time ETELS based on a sequence of Linearization points, and a continuous-time approximation based on Priestley’s evolutionary theory. The linearized evolutionary system is used to compute the response statistics of interest, including the first-passage probability, in first-order approximation. Numerical examples demonstrate the accuracy and limitations of the proposed Method.

  • tail equivalent Linearization Method for nonlinear random vibration
    Probabilistic Engineering Mechanics, 2007
    Co-Authors: Kazuya Fujimura, Armen Der Kiureghian
    Abstract:

    Abstract A new, non-parametric Linearization Method for nonlinear random vibration analysis is developed. The Method employs a discrete representation of the stochastic excitation and concepts from the first-order reliability Method, FORM. For a specified response threshold of the nonlinear system, the equivalent linear system is defined by matching the “design points” of the linear and nonlinear responses in the space of the standard normal random variables obtained from the discretization of the excitation. Due to this definition, the tail probability of the linear system is equal to the first-order approximation of the tail probability of the nonlinear system, this property motivating the name Tail-Equivalent Linearization Method (TELM). It is shown that the equivalent linear system is uniquely determined in terms of its impulse response function in a non-parametric form from the knowledge of the design point. The paper examines the influences of various parameters on the tail-equivalent linear system, presents an algorithm for finding the needed sequence of design points, and describes Methods for determining various statistics of the nonlinear response, such as the probability distribution, the mean level-crossing rate and the first-passage probability. Applications to single- and multi-degree-of-freedom, non-degrading hysteretic systems illustrate various features of the Method, and comparisons with results obtained by Monte Carlo simulations and by the conventional equivalent Linearization Method (ELM) demonstrate the superior accuracy of TELM over ELM, particularly for high response thresholds.