Multi-Criteria Design Optimization of Control System Instrumentation using Principal Component Analysis (PCA) and Structural Modeling Approaches
Zine-Eddine Meguetta1, Blaise Conrard2, Mireille Bayart3
1Zine-Eddine Meguetta, University of Lille Avenue Paul Langevin, Villeneuve d’Ascq, France.
2Blaise Conrard, is currently Assistant Professor and Senior Lecturer of Automatic Control Engineering Laboratory of University, Science and Technology and Polytechnic, Lille, France.
3Mireille Bayart, is currently a Professor of Production and Control Engineering with the LAGIS Laboratory of University, Science and Technology, Lille, France.
Manuscript received on November 28, 2014. | Revised Manuscript received on December 12, 2014. | Manuscript published on December 30, 2014. | PP: 125-132 | Volume-4 Issue-2, December 2014. | Retrieval Number: B3648124214/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Multi criteria design, Principal component analysis, Structural modeling, Optimization.