6533b85dfe1ef96bd12be930
RESEARCH PRODUCT
Velocity sensorless control of uncertain load using RKF tuned with an evolutionary algorithm and mu-analysis
Stephane CauxS. CarriereFrancesco AlongeMaurice Fadelsubject
Engineeringevolutionary algorithmOptimization algorithmbusiness.industrymotion controlEvolutionary algorithmrobust Kalman filterKalman filtermu-analysiMotion controlInstabilityMotion control ; Robustness ; OptimizationSettore ING-INF/04 - AutomaticaRobustness (computer science)Control theorySenseless controlbusinessActuatorrobustneoptimization[SPI.NRJ] Engineering Sciences [physics]/Electric powerdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2010-01-01 | IFAC Proceedings Volumes |