Multi-criteria design optimization of control system instrumentation using Principal Component Analysis (PCA) and structural modeling approaches

Zine-Eddine Meguetta (1, 2) , Blaise Conrard (2, 1) , Mireille Bayart (2, 1)

1 STF - Systèmes Tolérants aux Fautes
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

This article presents general approach of multi-criteria design of the control system instrumentation. The work reported here aims at defining that principal component analysis PCA can be used as method of design phase for non-linear system based on data measurements from the sensors and the available actuators for dynamical control system. The PCA consists to select inputs variables for quantifying the speed vt+δt using structural modeling, despite the environmental disturbance is the slope of the road and uncertainties in measurements from the sensors and actuators implemented in the control system instrumentation in design phase.

Mots clés: Multi criteria design; principal component analysis; structural modeling; optimization

Domaines: Automatique / Robotique; Sciences de l'ingénieur [physics]

Zine-Eddine Meguetta, Blaise Conrard, Mireille Bayart. Multi-criteria design optimization of control system instrumentation using Principal Component Analysis (PCA) and structural modeling approaches. International Journal of Engineering and Advanced Technology, 2014, 4 (2). ⟨hal-01413045⟩