A new dynamical fuzzy modeling and control for SISO complex systems

In this paper, we propose a new dynamical fuzzy modeling technique for SISO complex systems. It is seen that, the whole state-space of a complex system is divided into subspaces and a linear time-invariant (LTI) model is then used to approximate the complex system in each subspace. However, the global dynamical fuzzy model of the complex system is derived by aggregating all subsystem poles and zeros using the weighted average fuzzy inference approach instead of aggregating all subsystem parameter matrices. The motivation of such a modeling technique is to make the system and its global dynamical fuzzy model have the same stability property. The eigenstructure-assignment for linear time varying (LTV) systems and sliding mode control technique are then used to design a controller to stabilize the new global dynamical fuzzy system. A simulation example is given in support of the new dynamical fuzzy modeling and control.