MULTIVARIABLE FUZZY IDENTIFICATION OF UNMANNED AERIAL VEHICLES
Abstract
In this paper, a multivariable fuzzy identication methodology for Unmanned Aerial Vehicles
(UAVs) based on Observer/Kalman Filter Identication (OKID) and the Eigensystem Realization Algorithm
(ERA) is presented. The UAV is represented by a fuzzy Takagi-Sugeno (TS) model, whose antecedent is constituted by linguistic variables (fuzzy sets) and the consequent is constituted by linear sub-models in state-space discrete representation. The antecedent parameters are obtained using clustering fuzzy algorithms and the consequent parameters (state matrix, input matrix, output matrix and direct transition matrix) are obtained using the OKID/ERA-based algorithm discussed in details in this paper. In order to demonstrate its eciency, a comparative analysis of the multivariable fuzzy identication methodology presented in this paper and others methodologies accepted in the literature is performed in a traditional multivariable nonlinear benchmark system. In addition, experimental results for identication of a quadrotor UAV are presented, in order to illustrate the applicability of the methodology in a real system.