### Abstract

Original language | English |
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Publication status | Published - 2010 |

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### 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*.

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

Research output: Contribution to conference › Paper

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TY - CONF

T1 - Velocity Sensorless control of uncertain load using RKF tuned with an evolutionary algorithm and mu-analysis

AU - Alonge, Francesco

AU - Caux, Stéphane

AU - Fadel, Maurice

AU - Carrière, Sébastien

PY - 2010

Y1 - 2010

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.

UR - http://hdl.handle.net/10447/56845

M3 - Paper

ER -