The Experts below are selected from a list of 9 Experts worldwide ranked by ideXlab platform

Hassan Hammouri - One of the best experts on this subject based on the ideXlab platform.

  • Optimal input design for online identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

  • Optimal input design for on-line identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

Saida Flila - One of the best experts on this subject based on the ideXlab platform.

  • Optimal input design for online identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

  • Optimal input design for on-line identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

Pascal Dufour - One of the best experts on this subject based on the ideXlab platform.

  • Optimal input design for online identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

  • Optimal input design for on-line identification: a coupled observer-MPC approach
    2008
    Co-Authors: Saida Flila, Pascal Dufour, Hassan Hammouri
    Abstract:

    This paper presents a parametric sensitivity based controller for on line optimal Model Parameter identification using constrained closed loop control tools and an observer. In optimal input design problem, analytical solution exists for few particular cases based on a relatively simple Model. The approach proposed here may be used for a process based on a continuous Model in the time domain, with two assumptions on the observability and the general structure of the Model. The new proposed approach is to solve a Model predictive control problem coupled with an on line process Parameter estimation at each time using an observer. A dynamic parametric sensitivity Model (derived from the process Model) is also used on line to get the parametric sensitivity that has to be optimized. Both optimal input and Estimated Model Parameter are therefore obtained on line. The case study presented here is a powder coating curing process where the main thermal Parameter to identify influences the powder curing. First simulation results show here the efficiency of the approach in the control software (MPC@CB) developed under Matlab.

Robert L Bowden - One of the best experts on this subject based on the ideXlab platform.

  • an allee effect reduces the invasive potential of tilletia indica
    Phytopathology, 2002
    Co-Authors: Karen A Garrett, Robert L Bowden
    Abstract:

    Garrett, K. A., and Bowden, R. L. 2002. An Allee effect reduces the invasive potential of Tilletia indica. Phytopathology 92:1152-1159. The Karnal bunt pathogen, Tilletia indica, is heterothallic and depends on encounters on wheat spikes between airborne secondary sporidia of different mating types for successful infection and reproduction. This life history characteristic results in reduced reproductive success for lower population densities. Such destabilizing density dependence at low population levels has been described for a range of animals and plants and is often termed an Allee effect. Our objective was to characterize how the Allee effect might reduce the invasive potential of this economically important pathogen. We developed a simple population Model of T. indica that incorporates an Allee effect by calculating the probability of infection for different numbers of secondary sporidia in the infection court. An Allee effect is predicted to be important at the frontier of an invasion, for establishment of new foci by a small population of teliospores, and when the environment is nonconducive for the production of secondary sporidia. Using Estimated Model Parameter values, we demonstrated a theoretical threshold population size below which populations of T. indica were predicted to decline rather than increase. This threshold will vary from season to season as a function of weather variables and their effect on the reproductive potential of T. indica. Deployment of partial resistance or use of fungicides may be more useful if they push population levels below this threshold.

Karen A Garrett - One of the best experts on this subject based on the ideXlab platform.

  • an allee effect reduces the invasive potential of tilletia indica
    Phytopathology, 2002
    Co-Authors: Karen A Garrett, Robert L Bowden
    Abstract:

    Garrett, K. A., and Bowden, R. L. 2002. An Allee effect reduces the invasive potential of Tilletia indica. Phytopathology 92:1152-1159. The Karnal bunt pathogen, Tilletia indica, is heterothallic and depends on encounters on wheat spikes between airborne secondary sporidia of different mating types for successful infection and reproduction. This life history characteristic results in reduced reproductive success for lower population densities. Such destabilizing density dependence at low population levels has been described for a range of animals and plants and is often termed an Allee effect. Our objective was to characterize how the Allee effect might reduce the invasive potential of this economically important pathogen. We developed a simple population Model of T. indica that incorporates an Allee effect by calculating the probability of infection for different numbers of secondary sporidia in the infection court. An Allee effect is predicted to be important at the frontier of an invasion, for establishment of new foci by a small population of teliospores, and when the environment is nonconducive for the production of secondary sporidia. Using Estimated Model Parameter values, we demonstrated a theoretical threshold population size below which populations of T. indica were predicted to decline rather than increase. This threshold will vary from season to season as a function of weather variables and their effect on the reproductive potential of T. indica. Deployment of partial resistance or use of fungicides may be more useful if they push population levels below this threshold.