Identification of pneumatic artificial muscle manipulators by a MGA-based nonlinear NARX fuzzy model
This study investigates the technique of modeling and identification of a new dynamic NARX fuzzy model by means of genetic algorithms. In conventional identification techniques, there are difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model. All these factors deteriorate the identification performance when dealing with dynamic nonlinear industrial processes. To overcome these difficulties, this paper proposes a novel approach by using a modified genetic algorithm (MCA) combined with the predictive capability of nonlinear ARX (NARX) model for generating the dynamic NARX Takagi-Sugeno (TS) fuzzy model. The MCA algorithm processes the experimental input-output training data from the real system and optimizes the NARX fuzzy model parameters. This is referred to as fuzzy identification, which automatically generates the appropriate fuzzy if-then rules to characterize the dynamic nonlinear features of the real plant. The prototype pneumatic artificial muscle (PAM) manipulator, being a typical nonlinear and time-varying system, is used as a test system for this novel approach. This result shows that, with this MCA-based modeling and identification, the novel NARX fuzzy model identification approach to the PAM manipulator achieved highly outstanding performance and high precision as well. The accuracy of the proposed MGA-based NARX fuzzy model proves excellent in comparison with the MGA-based TS fuzzy model and the conventional GA-based TS fuzzy model. (C) 2008 Elsevier Ltd. All rights reserved.
modified genetic algorithm pam manipulator nonlinear arx model takagi-sugeno (ts) narx fuzzy model optimal identification