The Experts below are selected from a list of 141 Experts worldwide ranked by ideXlab platform

Stevan Dubljevic - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Output Regulator Design for a Coupled ODE-PDE System
    2020 American Control Conference (ACC), 2020
    Co-Authors: Guilherme Ozorio Cassol, Stevan Dubljevic
    Abstract:

    This manuscript addresses the design of a discrete regulator for an unstable coupled ODE-PDE cascade system with a recycle stream. The proposed regulator design considers a state feedback gain control law with the input applied to the ODE system. The controller has to ensure the closed-loop system stability and proper output tracking of reference signals. The discrete nature of the design is achieved by application of structure preserving Cayley-Tustin discretization to the coupled system given by a First-Order ODE and a First-Order hyperbolic PDE without the use of any spatial approximation and/or mODEl order reduction. For the stabilization, the backstepping methodology is applied to ensure the system is mapped to the desired stable target system. To achieve adequate tracking, an exosystem representation is assumed in the design and leads to the corresponding Sylvester equation. The corresponding relationship between the continuous and discrete setting is shown. Finally, the simulations show the performance of the designed regulator for proper stabilization and trajectory tracking.

Marie-eve Davoust - One of the best experts on this subject based on the ideXlab platform.

  • MODEl-Robust Design of Experiments for Sequential Identification of ODE Parameters
    2008
    Co-Authors: Hassan El Abiad, Laurent Le Brusquet, Marie-eve Davoust
    Abstract:

    This paper presents the idea of sequential mODEl-robust Design of Experiments (DOE) for the identification of dynamic systems mODEled with an Ordinary Differential Equation (ODE). The studied DOE problem consists in selecting sequentially the instants where the measures will be done in order to best estimate the system's parameter. The robustness is achieved by considering a statistical representation of the mODEl error defined as the difference between the true ODE and the ODE used in the mODEl. The idea of mODEling the mODEl error with a statistical representation has been widely explored in the DOE literature for the identification of static systems. However, there have been little previous works that apply this idea for the identification of dynamic systems. This paper initiates an exploration of this idea in the context of First-Order ODE. The mODEl error is mODEled by using a kernel-based representation (Gaussian process). A new criterion for the instant selection is constructed and tested on an illustrative example. The design reached with the proposed sequential robust criterion is compared with the design reached with the non-robust version of criterion and with the classical uniform design.

  • MODEl-robust Design of Experiments for sequential identification of ODE parameters
    2008 IEEE Workshop on Machine Learning for Signal Processing, 2008
    Co-Authors: Hassan El Abiad, Laurent Le Brusquet, Marie-eve Davoust
    Abstract:

    This paper presents the idea of sequential mODEl-robust design of experiments (DOE) for the identification of dynamic systems mODEled with an ordinary differential equation (ODE). The studied DOE problem consists in selecting sequentially the instants where the measures will be done in order to best estimate the systempsilas parameter. The robustness is achieved by considering a statistical representation of the mODEl error defined as the difference between the true ODE and the ODE used in the mODEl. The idea of mODEling the mODEl error with a statistical representation has been widely explored in the DOE literature for the identification of static systems. However, there have been little previous works that apply this idea for the identification of dynamic systems. This paper initiates an exploration of this idea in the context of First-Order ODE. The mODEl error is mODEled by using a kernel-based representation (Gaussian process). A new criterion for the instant selection is constructed and tested on an illustrative example. The design reached with the proposed sequential robust criterion is compared with the design reached with the non-robust version of criterion and with the classical uniform design.

Guilherme Ozorio Cassol - One of the best experts on this subject based on the ideXlab platform.

  • Discrete Output Regulator Design for a Coupled ODE-PDE System
    2020 American Control Conference (ACC), 2020
    Co-Authors: Guilherme Ozorio Cassol, Stevan Dubljevic
    Abstract:

    This manuscript addresses the design of a discrete regulator for an unstable coupled ODE-PDE cascade system with a recycle stream. The proposed regulator design considers a state feedback gain control law with the input applied to the ODE system. The controller has to ensure the closed-loop system stability and proper output tracking of reference signals. The discrete nature of the design is achieved by application of structure preserving Cayley-Tustin discretization to the coupled system given by a First-Order ODE and a First-Order hyperbolic PDE without the use of any spatial approximation and/or mODEl order reduction. For the stabilization, the backstepping methodology is applied to ensure the system is mapped to the desired stable target system. To achieve adequate tracking, an exosystem representation is assumed in the design and leads to the corresponding Sylvester equation. The corresponding relationship between the continuous and discrete setting is shown. Finally, the simulations show the performance of the designed regulator for proper stabilization and trajectory tracking.

Merry L. Lindsey - One of the best experts on this subject based on the ideXlab platform.

  • GENSiPS - Parameter distribution estimation in first order ODE
    2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013
    Co-Authors: Tianyi Yang, Nguyen Nguyen, Merry L. Lindsey
    Abstract:

    With development of new technologies applied to biological experiments, more and more data are generated every day. To make predictions in biological systems, mathematical mODEling plays a critical role. Ordinary differential equations (ODEs) contribute to a large portion in mathematical mODEling. In which parameters are inevitable. Noise is intrinsic in all experiments. Therefore, to think of parameters as statistical distributions is a realistic treatment. In this paper, we discuss in a 1st order ODE common in biological systems, how to calculate parameter distribution analytically according to the experimentally observed output assumed to be normal distribution. Conditions on when parameter can be correctly estimated are elucidated.

  • Parameter distribution estimation in first order ODE
    2013 IEEE International Workshop on Genomic Signal Processing and Statistics, 2013
    Co-Authors: Tianyi Yang, Nguyen Nguyen, Merry L. Lindsey
    Abstract:

    With development of new technologies applied to biological experiments, more and more data are generated every day. To make predictions in biological systems, mathematical mODEling plays a critical role. Ordinary differential equations (ODEs) contribute to a large portion in mathematical mODEling. In which parameters are inevitable. Noise is intrinsic in all experiments. Therefore, to think of parameters as statistical distributions is a realistic treatment. In this paper, we discuss in a 1st order ODE common in biological systems, how to calculate parameter distribution analytically according to the experimentally observed output assumed to be normal distribution. Conditions on when parameter can be correctly estimated are elucidated.

L.a.c.p. Da Mota - One of the best experts on this subject based on the ideXlab platform.

  • Determining Liouvillian first integrals for dynamical systems in the plane
    Computer Physics Communications, 2007
    Co-Authors: J. Avellar, L.g.s. Duarte, S. E. S. Duarte, L.a.c.p. Da Mota
    Abstract:

    Here we present/implement a semi-algorithm to find Liouvillian first integrals of dynamical systems in the plane. The algorithm is based on a Darboux-type procedure to find the integrating factor for the system. Since the particular form of such systems allows reducing it to a single rational first order ordinary differential equation (rational first order ODE), the Lsolver package presents a set of software routines in Maple for dealing with rational first order ODEs. The package present commands permitting research investigations of some algebraic properties of the system that is being studied.

  • Computer algebra solving of first order ODEs using symmetry methods
    Computer Physics Communications, 1997
    Co-Authors: E.s. Cheb-terrab, L.g.s. Duarte, L.a.c.p. Da Mota
    Abstract:

    Abstract A Maple V R.3/4 computer algebra package, ODEtools , for the analytical solving of first order ODEs using Lie group symmetry methods is presented. The set of commands includes a first order ODE solver and mutines for, among other things: the explicit determination of the coefficients of the infinitesimal symmetry generator; the construction of the most general invariant first order ODE under given symmetries; the determination of the canonical coordinates of the underlying invariant group; and the testing of the returned results.