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Yangquan Chen - One of the best experts on this subject based on the ideXlab platform.

  • robust stability check of fractional order linear time invariant systems with interval uncertainties
    Signal Processing, 2006
    Co-Authors: Yangquan Chen, Igor Podlubny
    Abstract:

    For uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients described in State Space Form, the robust stability check problem is solved for the first time in this paper. Both the checking procedure and the Matlab code are presented with two illustrative examples. The conservatism is shown to be small.

  • robust controllability of interval fractional order linear time invariant systems
    Signal Processing, 2006
    Co-Authors: Yangquan Chen
    Abstract:

    We consider uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients. Our focus is on the robust controllability issue for interval FO-LTI systems in State-Space Form. We revisit the controllability problem for the case when there is no interval uncertainty. It turns out that the controllability check for FO-LTI systems amounts to checking the controllability of conventional integer order State Space. Based on this fact, we further show that, for interval FO-LTI systems, the key is to check the linear dependency of a set of interval vectors. Illustrative examples are presented.

  • robust controllability of interval fractional order linear time invariant systems
    ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2005
    Co-Authors: Yangquan Chen
    Abstract:

    We consider uncertain fractional-order linear time invariant (FO-LTI) systems with interval coefficients. Our focus is on the robust controllability issue for interval FO-LTI systems in State-Space Form. We re-visited the controllability problem for the case when there is no interval uncertainty. It turns out that the stability check for FO-LTI systems amounts to checking the conventional integer order State Space using the same State matrix A and the input coupling matrix B. Based on this fact, we further show that, for interval FO-LTI systems, the key is to check the linear dependency of a set of interval vectors. Illustrative examples are presented.Copyright © 2005 by ASME

Neil Shephard - One of the best experts on this subject based on the ideXlab platform.

  • statistical algorithms for models in State Space Form ssfpack 3 0
    2008
    Co-Authors: Siem Jan Koopman, Neil Shephard, Jurgen A Doornik
    Abstract:

    SsfPack™ (Extended) version 3.0 is a suite of C routines for carrying out computations involving the statistical analysis of univariate and multivariate models in State Space Form with easy-to-use functions for Ox. SsfPack requires Ox 4 or above to run. SsfPack allows for a full range of different State Space Forms: from a simple time-invariant model to a complicated multivariate time-varying model. Functions are provided to put standard models such as SARIMA, unobserved components, time-varying regressions and cubic spline models into State Space Form. Basic functions are available for filtering, moment smoothing and simulation smoothing. Ready-to-use functions are provided for standard tasks such as likelihood evaluation, forecasting and signal extraction. SsfPack can be used for implementing, fitting and analysing Gaussian models relevant to many areas of econometrics and statistics. It provides all relevant tools for the treatment of non-Gaussian and nonlinear State Space models. In particular, tools are available to implement simulation based estimation methods such as importance sampling and Markov chain Monte Carlo (MCMC) methods.

  • inference for adaptive time series models stochastic volatility and conditionally gaussian State Space Form
    Econometric Reviews, 2006
    Co-Authors: Charles S Bos, Neil Shephard
    Abstract:

    In this paper we model the Gaussian errors in the standard Gaussian linear State Space model as stochastic volatility processes. We show that conventional MCMC algorithms for this class of models are ineffective, but that the problem can be alleviated by reparameterizing the model. Instead of sampling the unobserved variance series directly, we sample in the Space of the disturbances, which proves to lower correlation in the sampler and thus increases the quality of the Markov chain. Using our reparameterized MCMC sampler, it is possible to estimate an unobserved factor model for exchange rates between a group of n countries. The underlying n + 1 country-specific currency strength factors and the n + 1 currency volatility factors can be extracted using the new methodology. With the factors, a more detailed image of the events around the 1992 EMS crisis is obtained. We assess the fit of competitive models on the panels of exchange rates with an effective particle filter and find that indeed the factor mode...

  • inference for adaptive time series models stochastic volatility and conditionally gaussian State Space Form
    Research Papers in Economics, 2004
    Co-Authors: Charles S Bos, Neil Shephard
    Abstract:

    This discussion paper led to a publication in 'Econometric Reviews' , 2006, 25(2-3), 219-244. In this paper we replace the Gaussian errors in the standard Gaussian, linear State Space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression model. We also develop an effective particle filter for this model which is useful to assess the fit of the model.

  • inference for adaptive time series models stochastic volatility and conditionally gaussian State Space Form
    Social Science Research Network, 2004
    Co-Authors: Charles S Bos, Neil Shephard
    Abstract:

    In this paper we replace the Gaussian errors in the standard Gaussian, linear State Space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algorithms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression model. We also develop an effective particle filter for this model which is useful to assess the fit of the model.

  • analytic convergence rates and parameterization issues for the gibbs sampler applied to State Space models
    Journal of Time Series Analysis, 1999
    Co-Authors: Michael K Pitt, Neil Shephard
    Abstract:

    In this paper we obtain a closed Form expression for the convergence rate of the Gibbs sampler applied to an AR(1) plus noise model in terms of the parameters of the model. We also provide evidence that a ``centered'' parameterisation of a State Space model is preferable for the perFormance of the Gibbs sampler. These two results provide guidance when the Gaussianity or linearity of the State Space Form is lost. We illustrate this by examining the perFormance of a Markov Chain Monte Carlo sampler for the Stochastic Volatility model.

Yuanwei Tseng - One of the best experts on this subject based on the ideXlab platform.

  • Model-Following Designs Using Direct State Derivative Measurement Feedback in Novel Reciprocal State Space Form
    Journal of Applied Mathematics and Physics, 2019
    Co-Authors: Yuanwei Tseng
    Abstract:

    Model-Following Designs Using Direct State Derivative Measurement Feedback in Novel Reciprocal State Space Form

  • vibration tracking control of piezoelectric bimorph bender in novel reciprocal State Space Form
    IOP Conference Series: Materials Science and Engineering, 2018
    Co-Authors: Yuanwei Tseng, Chiachuan Tsai
    Abstract:

    In this paper, the deflection of a piezoelectric bimorph bender is controlled to track given time varying reference command solely utilizing direct acceleration measurement without integration. Novel estimator that directly applies acceleration measurement and controller that applies estimated State derivative signals have been developed in reciprocal State Space (RSS) Form. Simulations for the augmented system of both closed loop system and estimator have been carried out to successfully verify the proposed methods. The design approach in this paper is applicable to smart structures with accelerometers as sensors.

  • Sliding Mode Control with State Derivative Feedback in Novel Reciprocal State Space Form
    Advances and Applications in Nonlinear Control Systems, 2016
    Co-Authors: Yuanwei Tseng
    Abstract:

    This chapter introduces a novel reciprocal State Space (RSS) system Form. The concepts and the need of RSS Form are comprehensively reviewed and explained. It shows that in RSS Form, control design using State derivative related feedback is straightforward. Sliding mode control (SMC) is a nonlinear control design method and a highly active area of research. Finite-time convergence due to discontinuous control law, low sensitivity to plant parameter uncertainty and/or external perturbation, and greatly reduced-order modeling of plant dynamics are the main advantages of SMC. In the past, the majority of available SMC algorithms and the corresponding switching conditions involved only State related variables. In this chapter, the advantages of both RSS and SMC are combined to develop sliding mode control in RSS Form so that State derivate related feedback can be systematically applied in SMC to handle wider range of control problems. To provide the theoretical foundation, stability analysis in RSS Form is first reviewed. Next, novel switching function and approaching condition based on the derivative of sliding surface are proposed to carry out SMC design approach in RSS Form with considerations of system uncertainty and disturbance. In addition, algorithm of finding upper bound of system uncertainty is developed for robustness analysis. To verify the proposed design algorithms, numerical examples are provided. Finally, conclusions are drawn.

  • control design for system with lipschitz nonlinearity of State derivative variables in reciprocal State Space Form
    International Conference on Image Processing, 2015
    Co-Authors: Yuanwei Tseng
    Abstract:

    This paper addresses the need of the novel reciprocal State Space (RSS) Form to be the supplement of standard State Space system in control designs. Controller is designed utilizing State derivative feedback alone in RSS Form for systems with Lipschitz nonlinearity. It will show that the design procedure is straightforward. It will also show via a real electric circuit that control for a class of singular systems with impulse modes can easily be carried out using the proposed design method. The purpose of this paper is to develop novel and simple method based on State derivative feedback so that wider ranges of problems can be solved without too much of mathematics overhead.

  • sliding mode control with State derivative output feedback in reciprocal State Space Form
    Abstract and Applied Analysis, 2013
    Co-Authors: Yuanwei Tseng, Yuning Wang
    Abstract:

    This paper investigates the novel sliding mode control design with State derivative output feedback in nontraditional reciprocal State Space (RSS) Form. The concepts and the need of RSS Form are comprehensively reviewed and explained. Novel switching function and approaching condition based on the derivative of sliding surface are introduced. In addition, a sufficient condition for finding the upper bound of system uncertainty to guarantee the stability in sliding surface is developed for robustness analysis. A compact sliding mode controller utilizing only State derivative related output feedback is proposed for systems with system uncertainty, matched input uncertainty, and matched external disturbance. Simulation results for a circuit system successfully verify the validities of the proposed algorithms. Our derivation is basically parallel to that for systems in standard State Space Form. Therefore, those who understand the concepts of sliding mode control can easily apply our method to handle more control problems without being involved in complex mathematics.

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

  • sliding mode control with State derivative output feedback in reciprocal State Space Form
    Abstract and Applied Analysis, 2013
    Co-Authors: Yuanwei Tseng, Yuning Wang
    Abstract:

    This paper investigates the novel sliding mode control design with State derivative output feedback in nontraditional reciprocal State Space (RSS) Form. The concepts and the need of RSS Form are comprehensively reviewed and explained. Novel switching function and approaching condition based on the derivative of sliding surface are introduced. In addition, a sufficient condition for finding the upper bound of system uncertainty to guarantee the stability in sliding surface is developed for robustness analysis. A compact sliding mode controller utilizing only State derivative related output feedback is proposed for systems with system uncertainty, matched input uncertainty, and matched external disturbance. Simulation results for a circuit system successfully verify the validities of the proposed algorithms. Our derivation is basically parallel to that for systems in standard State Space Form. Therefore, those who understand the concepts of sliding mode control can easily apply our method to handle more control problems without being involved in complex mathematics.

Igor Podlubny - One of the best experts on this subject based on the ideXlab platform.