Velocity Sensorless control of uncertain load using RKF tuned with an evolutionary algorithm and mu-analysis

Francesco Alonge, Stéphane Caux, Maurice Fadel, Sébastien Carrière

    Research output: Contribution to conferencePaper

    3 Citations (Scopus)

    Abstract

    In case of a velocity control scheme for a load directly driven by an actuator, large variations of its parameters are problematic due to possible instability and large variations of the final performances. This performances are then decreasing if a sensorless control is implemented due to cost, reliability or application constraints. This paper proposes solutions to quickly and accurately tune an observer with a lower computer time consumption and lower conception time. A previous calculated state feedback is used as base for a Kalman filter with special noise matrices. An evolutionary algorithm optimizes the observers degrees of freedom all over the variations. The mu-analysis theory helps to cancel known unstable set of parameters before running iterations in the optimization algorithm. Experiments show that the stability and the performance are effectively maintained.
    Original languageEnglish
    Publication statusPublished - 2010

    Fingerprint

    Evolutionary algorithms
    Velocity control
    State feedback
    Kalman filters
    Actuators
    Costs
    Experiments
    Sensorless control

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering

    Cite this

    Velocity Sensorless control of uncertain load using RKF tuned with an evolutionary algorithm and mu-analysis. / Alonge, Francesco; Caux, Stéphane; Fadel, Maurice; Carrière, Sébastien.

    2010.

    Research output: Contribution to conferencePaper

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    abstract = "In case of a velocity control scheme for a load directly driven by an actuator, large variations of its parameters are problematic due to possible instability and large variations of the final performances. This performances are then decreasing if a sensorless control is implemented due to cost, reliability or application constraints. This paper proposes solutions to quickly and accurately tune an observer with a lower computer time consumption and lower conception time. A previous calculated state feedback is used as base for a Kalman filter with special noise matrices. An evolutionary algorithm optimizes the observers degrees of freedom all over the variations. The mu-analysis theory helps to cancel known unstable set of parameters before running iterations in the optimization algorithm. Experiments show that the stability and the performance are effectively maintained.",
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    AU - Alonge, Francesco

    AU - Caux, Stéphane

    AU - Fadel, Maurice

    AU - Carrière, Sébastien

    PY - 2010

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    N2 - In case of a velocity control scheme for a load directly driven by an actuator, large variations of its parameters are problematic due to possible instability and large variations of the final performances. This performances are then decreasing if a sensorless control is implemented due to cost, reliability or application constraints. This paper proposes solutions to quickly and accurately tune an observer with a lower computer time consumption and lower conception time. A previous calculated state feedback is used as base for a Kalman filter with special noise matrices. An evolutionary algorithm optimizes the observers degrees of freedom all over the variations. The mu-analysis theory helps to cancel known unstable set of parameters before running iterations in the optimization algorithm. Experiments show that the stability and the performance are effectively maintained.

    AB - In case of a velocity control scheme for a load directly driven by an actuator, large variations of its parameters are problematic due to possible instability and large variations of the final performances. This performances are then decreasing if a sensorless control is implemented due to cost, reliability or application constraints. This paper proposes solutions to quickly and accurately tune an observer with a lower computer time consumption and lower conception time. A previous calculated state feedback is used as base for a Kalman filter with special noise matrices. An evolutionary algorithm optimizes the observers degrees of freedom all over the variations. The mu-analysis theory helps to cancel known unstable set of parameters before running iterations in the optimization algorithm. Experiments show that the stability and the performance are effectively maintained.

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