In this paper, we present the design of a new unknown-input observer for Takagi–Sugeno
fuzzy systems with unmeasurable premise variables. The work proposes an improvement of …

This paper presents a new adaptive nonlinear proportional integral derivative radial basis
function neural network (NPID-RBFNN) for ballbots with external force. The proposed …

This work presents a contribution to reduce the conservatism in nonlinear observer for
TakagiSugeno fuzzy systems with unmeasurable premise variables based on the mean …

This paper proposes a new strategy based on linear matrix inequalities (LMIs) for the
synthesis of state-and output-feedback controllers through parallel distributed compensation …

This article aims to design observers for the purpose of simultaneously estimating the fault
and states of a class of Lipschitz nonlinear systems and attenuating the effect of …

In this work, we present the design of an observer for Takagi–Sugeno fuzzy systems with
unmeasurable premise variables. Moving away from Lipschitz-based and L 2 attenuation …

We propose approaches regarding observer designs for nonlinear systems represented by
Takagi-Sugeno fuzzy model in the case that premise variables are unmeasurable. In the …

Fault occurrence in practical systems, if not addressed, can cause diminished performance
or even system breakdown. Therefore, fault detection has emerged as a crucial challenge in …

This study introduces a fault estimation observer for nonlinear systems, employing a
proportional-integral approach, convex optimization techniques, and quadratic Lyapunov …

In this work, a contribution to reduce the conservatism of the observers for Takagi-Sugeno
fuzzy systems with unmeasurable premise variables (UPV) based on the Lipschitz …