Online identification of evolving Takagi–Sugeno–Kang fuzzy models for crane systems

• Published in: Applied soft computing 2014-11, Vol.24, p.1155-1163
• Main Authors: Precup, Radu-Emil, Filip, Horaţiu-Ioan, Rădac, Mircea-Bogdan, Petriu, Emil M., Preitl, Stefan, Dragoş, Claudia-Adina
• Format: Article
• Language: English
• Subjects: Crane systems; Evolving Takagi–Sugeno–Kang fuzzy models; Online identification algorithms; Pendulum–crane laboratory equipment; Potentials of new data points; Sum of squared errors
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.01.013

•New evolving Takagi–Sugeno–Kang (TSK) fuzzy models dedicated to crane systems are proposed.•A set of three evolving TSK fuzzy models are derived by an online identification algorithm.•The models are tested and compared against the experimental data on a pendulum–crane equipment.•The proposed TSK fuzzy models are simple and consistent with both training data and testing data. This paper suggests new evolving Takagi–Sugeno–Kang (TSK) fuzzy models dedicated to crane systems. A set of evolving TSK fuzzy models with different numbers of inputs are derived by the novel relatively simple and transparent implementation of an online identification algorithm. An input selection algorithm to guide modeling is proposed on the basis of ranking the inputs according to their important factors after the first step of the online identification algorithm. The online identification algorithm offers rule bases and parameters which continuously evolve by adding new rules with more summarization power and by modifying existing rules and parameters. The potentials of new data points are used with this regard. The algorithm is applied in the framework of the pendulum–crane system laboratory equipment. The evolving TSK fuzzy models are tested against the experimental data and a comparison with other TSK fuzzy models and modeling approaches is carried out. The comparison points out that the proposed evolving TSK fuzzy models are simple and consistent with both training data and testing data and that these models outperform other TSK fuzzy models.