A generalized interval type-۲ adaptive neuro-fuzzy framework for vibration suppression of uncertain building structure

A novel combinatorial adaptive type-۲ neuro-fuzzy soft computing technique and robust proportional integral derivative (PID) framework for intelligent structural control is presented. The fuzzy logic controllers (FLCs), are designed independently of the mathematical model of the system. The type-۱ FLCs, have a limited ability to reduce the effect of uncertainty, due to their fuzzy sets with a crisp degree of membership. In real applications, the consequent part of the fuzzy rules is uncertain. The type-۲ FLCs, are robust to the fuzzy rules and the process parameters due to the fuzzy degree of membership functions and footprint of uncertainty (FOU). The adaptivity of the proposed method is provided with the optimum tuning of the parameters using the neural network training algorithms. In this approach, The PID control force is obtained using the generalized type-۲ neuro-fuzzy in such a way that the stability and robustness of the controller are guaranteed. The robust performance and stability of the presented framework are demonstrated in a numerical study for an eleven-story seismically-excited building structure combined with an active tuned mass damper (ATMD). The results indicate that the introduced type-۲ neuro-fuzzy PID control scheme is effective to attenuate plant states in the presence of the structured and unstructured uncertainties, compared to the conventional, type-۱ FLC, type-۲ FLC, and type-۱ neuro-fuzzy PID controllers.

Keywords:

soft computing , uncertainty , type-۲ FLC , the footprint of uncertainty , type-۲ neuro-fuzzy PID.