Evolving Fuzzy and Tensor Product-based Models for Tower Crane Systems

This paper derives several nonlinear models of a family of nonlinear tower crane systems. First, the state-space model is improved using six parameters, which are optimally tuned using a metaheuristic Grey Wolf Optimizer algorithm. Second, fuzzy models are obtained separately for the three system outputs using an incremental online identification algorithm that develops evolving Takagi-Sugeno-Kang fuzzy models. Third, the derivation of a Tensor Product (TP)-based model is conducted. The behaviors of the tower crane systems, the evolving fuzzy models, the TP-based model and the first principles model are tested in a different scenario to the parameter identification one, and the outputs are measured. The experimental results on tower crane laboratory equipment and the comparison show the good performance of the nonlinear models derived for this challenging process and their potential for model-based control.